<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GOM-Based Compatible Substitutions Optimization for Variable-Length Representation Gray-Box Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference (GECCO '25 Companion)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/GOM-BasedCompatibleSubstitutionsOptimization.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Effective recombination operators utilizing interdependence of genes ensure that specific arrangements or combinations of genes are preserved, allowing offspring to inherit beneficial traits from both parents without disrupting important gene interactions. However, such operators are easiest to implement for fixed-length genetic representations such as vectors of genes. In this work, we show that for some problems with variable-length representations, it is possible to design an algorithm that employs the GOM (Gene-pool Optimal Mixing) operator without the need to learn dependencies between specific genes. Instead, our approach - Compatible Substitutions Optimization (CoSO) - leverages expert-driven models of compatible substitutions that take advantage of the characteristics of the representation. Our experiments indicate that the proposed method performs better than standard evolutionary algorithms on a problem of evolving tall 3D structures, while also providing significant potential for further enhancements.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sofya Aksenyuk</style></author><author><style face="normal" font="default" size="100%">Szymon Bujowski</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Late Bloomers, First Glances, Second Chances: Exploration of the Mechanisms Behind Fitness Diversity</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference (GECCO '24)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/FitnessDiversityMechanismsInHFCAndConvectionSel.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fitness diversity is an idea in the field of evolutionary algorithms, which calls for supporting the evolution of solutions at all fitness levels simultaneously. In some cases, this idea may even extend to cultivating the worst solutions. While this may seem counterintuitive, fitness diversity has shown its promise in algorithms such as Hierarchical Fair Competition and Convection Selection. Although these algorithms share many similarities, the role fitness diversity serves in each of them is different. In Hierarchical Fair Competition, fitness diversity facilitates a constant incorporation of novel genotypes into the solutions that are already good - a mechanism we dub First Glances - and discovery of solutions through the exploration of neutral networks of different fitness levels - which we name Late Bloomers. On the other hand, Convection Selection uses fitness diversity techniques to give broken solutions time and shelter necessary to cross larger valleys in the fitness landscape - a mechanism we call Second Chances. In this work, we compare these two algorithms and their respective mechanisms over a range of numerical and 3D structure design optimization problems. We analyze the extent to which their mechanisms are utilized, and measure the impact of these mechanisms on finding good solutions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Revealing the Inner Dynamics of Evolutionary Algorithms with Convection Selection</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference Companion (GECCO '23)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/InnerDynamicsOfConvectionSelection.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Lisbon, Portugal</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Evolutionary algorithms are stochastic algorithms so they tend to find different solutions when run repeatedly. However, it is not just the solutions that vary - the very dynamics of the search that led to finding these solutions are likely to differ as well. It is especially in the algorithms with complex population structures - such as convection selection where a population is divided into subpopulations according to fitness values - where an opportunity for highly diverse dynamics arises. This work investigates the way evolutionary dynamics of subpopulations influence the performance of evolutionary algorithms with convection selection. We employ a demanding task of evolutionary design of 3D structures to analyze the relation between the properties of the optimization task and the features of the evolutionary process. Based on this analysis, we identify the mechanisms that influence the performance of convection selection, and suggest ways to improve this selection scheme.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adam Klejda</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Agnieszka Mensfelt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diversification Techniques and Distance Measures in Evolutionary Design of 3D Structures</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference Companion (GECCO '22)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Evolutionary algorithms are among the most successful metaheuristics for hard optimization problems. Nonetheless, there is still much room for improvement of their effectiveness, especially in the multimodal problems, where the algorithms are prone to falling into unsatisfactory local optima. One of the solutions to this problem may be to encourage a broader exploration of the solution space. Motivated by this premise, we compare the evolutionary algorithm without niching, with niching, the novelty search, and the two-criteria optimization (NSGA-II) where the criteria of fitness and diversity are not aggregated. We investigate these methods in the context of automated design of three-dimensional structures, which is one of the hardest optimization problems, often characterized by a rugged fitness landscape arising from a complex genotype to phenotype mapping. In the experiments we optimize 3D structures towards two different goals, height and velocity, using two genetic encodings and three distance measures: two phenetic ones and a genetic one. We demonstrate how different distance measures and diversity promotion mechanisms influence the fitness of the obtained solutions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Basiukajc</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fitness Diversification in the Service of Fitness Optimization: a Comparison Study</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference Companion (GECCO '22)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/FitnessDiversity.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Blindly chasing after fitness is not the best strategy for optimization of hard problems, as it usually leads to premature convergence and getting stuck in low-quality local optima. Several techniques such as niching or quality-diversity algorithms have been established that aim to alleviate the selective pressure present in evolutionary algorithms and to allow for greater exploration. Yet another group of methods which can be used for that purpose are fitness diversity methods. In this work we compare the standard single-population evolution against three fitness diversity methods: fitness uniform selection scheme (FUSS), fitness uniform deletion scheme (FUDS), and convection selection (ConvSel). We compare these methods on both mathematical and evolutionary design benchmarks over multiple parametrizations. We find that given the same computation time, fitness diversity methods regularly surpass the performance of the standard single-population evolutionary algorithm.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Agnieszka Mensfelt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human perception of similarity of 3D graph structures</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This report describes the study of how humans perceive similarity of simple three-dimensional graph structures. Participants of this study were required to align pairs of 3D structures the best they could, then match all vertices of these structures, evaluate their perceived similarity on a numerical scale, and justify their decisions as a textual response. The outcomes of this process were analyzed and compared to the outcomes of a heuristic computer algorithm that maximized the alignment of pairs of 3D structures and matched their vertices. The influence of personal characteristics of participants such as their gender, age, handedness, education, but also time required to complete each task, on the quality of the matching of vertices was evaluated. The consistency of human responses was also verified. The participants turned out to be more consistent (both between themselves and with the algorithm) in the degree of similarity estimated than in matching of vertices. Personal characteristics of the subjects did not have an influence on their similarity assessments.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Tomasz Żok</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Morality, protection, security and gain: lessons from a minimalistic, economically inspired multi-agent model</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computing and Decision Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">17–33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, we introduce a simple multi-agent simulation model with two roles of agents that correspond to moral and immoral attitudes. The model is given explicitly by a set of mathematical equations with continuous variables and is characterized by four parameters: morality, protection, and two efficiency parameters. Agents are free to adjust their roles to maximize individual gains. The model is analyzed theoretically to find conditions for its stability, i.e., the fractions of agents of both roles that lead to an equilibrium in their gains. A multi-agent simulation is also developed to verify the dynamics of the model for all values of morality and protection parameters, and to identify potential discrepancies with the theoretical analysis.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Człowieczeństw0</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mooncoder.com/czlowieczenstw0</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Humann3ss</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mooncoder.com/humann3ss</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Iwo Błądek</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mappism: formalizing classical and artificial life views on mind and consciousness</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computing and Decision Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/MappismConsciousness.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">55–99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Harold Fellermann</style></author><author><style face="normal" font="default" size="100%">Jaume Bacardit</style></author><author><style face="normal" font="default" size="100%">Angel Goni-Moreno</style></author><author><style face="normal" font="default" size="100%">Rudolf M. Füchslin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring properties of movement in populations of evolved 3D agents</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life Conference Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pages><style face="normal" font="default" size="100%">485–492</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parametrizing Convection Selection: Conclusions from the Analysis of Performance in the NKq Model</style></title><secondary-title><style face="normal" font="default" size="100%">Genetic and Evolutionary Computation Conference (GECCO '19), July 13–17</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/ConvectionSelectionNKqModel.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Prague, Czech Republic</style></pub-location><pages><style face="normal" font="default" size="100%">804–811</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Convection selection in evolutionary algorithms is a method of splitting the population into subpopulations based on the fitness values of solutions. Convection selection was previously found to be superior to standard selection techniques in difficult tasks of evolutionary design. However, reaching its full potential requires tuning of parameters that affect the performance of the evolutionary search process. Performing experiments on benchmark fitness functions does not provide general knowledge required for such tuning. Therefore, in order to gain an insight into the link between the characteristics of the fitness landscape, the parameters of the selection technique, and the quality of the best found solutions, we perform an analysis based on the NKq model of rugged fitness landscapes with neutrality. As a result, we identify several rules that will help researchers and practitioners of evolutionary algorithms adjust the values of convection selection parameters based on the knowledge of the properties of a given optimization problem.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Gorgolewski</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author><author><style face="normal" font="default" size="100%">Krzysztof Rosinski</style></author><author><style face="normal" font="default" size="100%">Paweł Rychły</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Properties of movement of 3D agents</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/PropertiesOfMovementOf3DAgents.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA-1/2019</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of the tournament-based convection selection with the island model in evolutionary algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/ConvectionSelectionVsIslandModel.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">106–114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Convection selection is an approach to multipopulational evolutionary algorithms where solutions are assigned to subpopulations based on their fitness values. Although it is known that convection selection can allow the algorithm to find better solutions than it would be possible with a standard single population, the convection approach was not yet compared to other, commonly used architectures of multipopulational evolutionary algorithms, such as the island model. In this paper we describe results of experiments which facilitate such a comparison, including extensive multi-parameter analyses. We show that approaches based on convection selection can obtain better results than the island model, especially for difficult optimization problems such as those existing in the area of evolutionary design. We also introduce and test a generalization of the convection selection which allows for adjustable overlapping of fitness ranges of subpopulations; the amount of overlapping influences the exploration vs. exploitation balance.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Konrad Miazga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tournament-based convection selection in evolutionary algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">PPAM 2017 proceedings, Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/TournamentBasedConvectionSelectionEvolutionary.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the problems that single-threaded (non-parallel) evolutionary algorithms encounter is premature convergence and the lack of diversity in the population. To counteract this problem and improve the performance of evolutionary algorithms in terms of the quality of optimized solutions, a new subpopulation-based selection scheme - the convection selection - is introduced and analyzed in this work. This new selection scheme is compared against traditional selection of individuals in a single-population evolutionary processes. The experimental results indicate that the use of subpopulations with fitness-based assignment of individuals yields better results than both random assignment and a traditional, non-parallel evolutionary architecture.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Universes and simulations: civilizational development in nested embedding</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computing and Decision Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/DevelopmentNestedUniverses.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">181–205</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The rapid development of technology has allowed computer simulations to become routinely used in an increasing number of fields of science. These simulations become more and more realistic, and their energetic efficiency grows due to progress in computer hardware and software. As humans merge with machines via implants, brain-computer interfaces and increased activity involving information instead of material objects, philosophical concepts and theoretical considerations on the nature of reality are beginning to concern practical, working models and testable virtual environments. This article discusses how simulation is understood and employed in computer science today, how software, hardware and the physical universe unify, how simulated realities are embedded one in another, how complicated it can get in application, practical scenarios, and the possible consequences of these situations. A number of basic properties of universes and simulations in such multiply nested structures are reviewed, and the relationship of these properties with a level of civilizational development is explored.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Applications of a similarity measure in the analysis of populations of 3D agents</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/SimilarityPopulations3DAgents.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">407–418</style></pages><abstract><style face="normal" font="default" size="100%">Research in complex collective and multi-agent systems often involves building models of three-dimensional biological life or evolving such structures in virtual environments. Applications stemming from evolutionary design, engineering, robotics, and artificial life require processing of large numbers of such agents that are encoded in some form of a &quot;genotype&quot;. However, what is important in evaluation is the &quot;phenotype&quot;, i.e. the actual 3D body and its properties. This work introduces a number of ways in which a measure of similarity of 3D agents can support researchers in recognizing the link between the genotype and phenotype spaces, building taxonomies of 3D bodies and automatically selecting representative agents. The measure of similarity employed here is based on phenotypes and places few restrictions on the compared designs, so it can be applied independently of genetic representation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chlebowski, Szymon</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Generation of Erotetic Search Scenarios: Classification, Optimization, and Knowledge Extraction</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Computational Logic</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">knowledge extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic of questions</style></keyword><keyword><style  face="normal" font="default" size="100%">multicriteria analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">optimal erotetic scenario</style></keyword><keyword><style  face="normal" font="default" size="100%">rule mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.put.poznan.pl/mkomosinski/research/automated-erotetic-search-scenarios.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">8:1–8:27</style></pages><abstract><style face="normal" font="default" size="100%">This paper concerns automated generation and processing of erotetic search scenarios (ESSs). ESSs are formal constructs characterized in Inferential Erotetic Logic that enable finding possible answers to a posed question by decomposing it into auxiliary questions. The first part of this work describes a formal account on ESSs. The formal approach is then applied to automatically generate ESSs, and the resulting scenarios are evaluated according to a number of criteria. These criteria are subjected to discordance analysis that reveals their mutual relationships. Finally, knowledge concerning relationships between different values of evaluation criteria is extracted by applying Apriori - an association rules mining algorithm. The proposed approach of integration of formal erotetic logic with computational tools provides an extensive insight into the former and helps with the development of efficient ESSs.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary construction of derivations in classical propositional logic using a symbolic-connectionist representation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/EvolutionOfDerivationsInLogic.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA–3/17</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">This report introduces a way derivations in classical propositional logic can be constructed using evolutionary algorithms. The derivations are represented by connectionist systems. There are three kinds of nodes constituting these systems: formula nodes that generate signal in the form of strings of symbols, &quot;modus ponens&quot; nodes that transform incoming signal according to the &quot;modus ponens&quot; rule, and substitution nodes that transform incoming signal by applying the substitution rule. This work presents initial research on an approach that is a part of our quest for efficient construction of derivations using various logics and constrained in various ways. The final part of this report outlines limitations encountered in our initial experiments and the ways the proposed approach can be improved.</style></abstract><work-type><style face="normal" font="default" size="100%">Research report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paweł Topa</style></author><author><style face="normal" font="default" size="100%">Łukasz Faber</style></author><author><style face="normal" font="default" size="100%">Jarosław Tyszka</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modelling ecology and evolution of Foraminifera in the agent-oriented distributed platform</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jocs.2016.07.009</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">69–84</style></pages><abstract><style face="normal" font="default" size="100%">We present a new software platform called eVolutus for simulating evolution of living organisms. We choose Foraminifera as model organisms that represent a group of single-cellular, mainly marine, organisms that construct well fossilisable protective shells. They have lived on Earth for more than 540 million years and have left an extraordinary fossil record that is excellent for testing palaeoecological and evolutionary hypotheses. We use the AgE platform, which is a lightweight agent-oriented platform supporting distributed computation. The paper presents the general architecture of this modelling environment as well as more detailed descriptions of the implemented rules and applied solutions. The utility of this software is demonstrated by presenting the configuration and results of sample experiments.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Agnieszka Mensfelt</style></author><author><style face="normal" font="default" size="100%">Jarosław Tyszka</style></author><author><style face="normal" font="default" size="100%">Jan Goleń</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-agent simulation of benthic foraminifera response to annual variability of feeding fluxes</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/SimulationForaminiferaFeedingFluxes.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">419–431</style></pages><abstract><style face="normal" font="default" size="100%">In this work we describe a novel simulation model of foraminifera and their microhabitat. The simulations reported here are focused on the response of foraminiferal populations to environmental feeding fluxes. The experiments allowed to calibrate the model and to simulate realistic population patterns known from culture experiments, as well as from oceanographic and paleoecologic studies. Variability of annual food flux has a direct impact on productivity of foraminifera: population sizes closely follow the intensity of constant and seasonal food fluxes in both scenarios. This correlation between the food influx and population size is interpreted as the consequence of changing the carrying capacity of the system. Seasonal pulses of particulate organic matter enhance the population size which is represented by a higher number of fossilized shells. Our model offers a flexible experimental design to run sophisticated in silico experiments. This approach reveals a novel methodology for testing sensitivity of fossil and recent foraminiferal assemblages to environmental changes. Furthermore, it facilitates predictive applications for monitoring studies based on simulation of various scenarios.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Szymon Ulatowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multithreaded computing in evolutionary design and in artificial life simulations</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of Supercomputing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/MultithreadedEvolutionaryDesign.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">73</style></volume><pages><style face="normal" font="default" size="100%">2214–2228</style></pages><abstract><style face="normal" font="default" size="100%">This article investigates low-level and high-level multithreaded performance of evolutionary processes that are typically employed in evolutionary design and artificial life. Computations performed in these areas are specific because evaluation of each genotype usually involves time-consuming simulation of virtual environments and physics. Computational experiments have been conducted using the Framsticks simulator running a multithreaded version of a standard evolutionary experiment. Tests carried out on five diverse machines and two operating systems demonstrated how low-level performance depends on the number of physical and logical CPU cores and on the number of threads. Two string implementations have been compared, and their raw performance turned out to fundamentally differ in a multithreading setup. To improve high-level performance of parallel evolutionary algorithms, i.e. the quality of optimized solutions, a new distribution scheme that is especially useful and efficient for complex representations of solutions – the convection distribution – has been introduced. This new distribution scheme has been compared against a random distribution of genotypes among threads that carry out evolutionary processes.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Agnieszka Mensfelt</style></author><author><style face="normal" font="default" size="100%">Paweł Topa</style></author><author><style face="normal" font="default" size="100%">Jarosław Tyszka</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gruca, Aleksandra</style></author><author><style face="normal" font="default" size="100%">Brachman, Agnieszka</style></author><author><style face="normal" font="default" size="100%">Kozielski, Stanisław</style></author><author><style face="normal" font="default" size="100%">Czachórski, Tadeusz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of a morphological similarity measure to the analysis of shell morphogenesis in Foraminifera</style></title><secondary-title><style face="normal" font="default" size="100%">Man–Machine Interactions 4</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Intelligent Systems and Computing</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/ForaminiferaGenotypePhenotypeMapping.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">391</style></volume><pages><style face="normal" font="default" size="100%">215–224</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-23436-6</style></isbn><abstract><style face="normal" font="default" size="100%">This work evaluates the genotype-to-phenotype mapping defined by one of the models of growth of foraminifera. Foraminifera are simple unicellular organisms with very diverse morphologies. To analyze the mapping, a morphological similarity measure is needed that compares 3D structures. One of the key components of the similarity estimation algorithm is Singular Value Decomposition (SVD). Since this algorithm is heavily used and its performance is important, four SVD implementations have been compared in this work. Distance matrices of the phenotypes obtained for equally distant genotypes were computed using the similarity measure. For the visualization of the phenotype space, multidimensional scaling techniques were used. Visual comparison of the genotype and the phenotype spaces revealed characteristics and potential weaknesses of the analyzed model of foraminifera growth, and demonstrated usefulness of the proposed approach.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Agnieszka Mensfelt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Emotions perceived and emotions experienced in response to computer-generated music</style></title><secondary-title><style face="normal" font="default" size="100%">Music Perception</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.cs.put.poznan.pl/mkomosinski/research/music-emotions-perceived-and-experienced.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">UC Press</style></publisher><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">432–445</style></pages><abstract><style face="normal" font="default" size="100%">This paper explores perceived and experienced emotions elicited by computer-generated music. During the experiments, 30 participants listened to 20 excerpts. Each of the excerpts lasted for about 16 seconds and was generated in real-time by specifically designed software. Measurements were performed using both categorical (a free verbal description) and dimensional approaches. The relationship between structural factors of music (mode, tempo, pitch height, rhythm, articulation and presence of the dissonance) and emotions was examined. Personal characteristics of the listener: gender and musical training were also taken into account. The relationship between structural factors and the perceived emotions was mostly congruent with predictions derived from the literature, and the relationship between those factors and experienced emotions was very similar. Tempo and pitch height – the cues common to music and speech – turned out to have a strong influence on the evaluation of emotion. Personal factors had a marginal effect. In the case of verbal categories comparable with the dimensional model, a strong correspondence was found.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paweł Topa</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Maciej Bassara</style></author><author><style face="normal" font="default" size="100%">Jarosław Tyszka</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gruca, Aleksandra</style></author><author><style face="normal" font="default" size="100%">Brachman, Agnieszka</style></author><author><style face="normal" font="default" size="100%">Kozielski, Stanisław</style></author><author><style face="normal" font="default" size="100%">Czachórski, Tadeusz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">eVolutus: a configurable platform designed for ecological and evolutionary experiments tested on Foraminifera</style></title><secondary-title><style face="normal" font="default" size="100%">Man–Machine Interactions 4</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Intelligent Systems and Computing</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-23437-3_23</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">391</style></volume><pages><style face="normal" font="default" size="100%">269–278</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-23436-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nesting</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/nesting</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zagnieżdżenie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/zagniezdzenie</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Piotr Szachewicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic species counterpoint composition by means of the dominance relation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Mathematics and Music</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.put.poznan.pl/mkomosinski/research/counterpoint-composition-dominance-relation.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">75-94</style></pages><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mobile health: assessment of upper limb motor function via a drawing test on a mobile device</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.put.poznan.pl/mkomosinski/research/parkinson-mobile-evaluation-app.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA–7/15</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">This paper describes and compares experiences with two generations of devices that can acquire pen movement data from a drawing test. The first part of this work summarizes methods introduced earlier that analyze data acquired using a graphics tablet from healthy subjects and people with Parkinson's disease before and after surgery. These methods can discriminate groups of patients and can assess improvements after surgery. The second part of this work presents the implementation of analogous methods that estimate hand tremor and drawing smoothness on a commercially available mobile tablet. Both approaches and applications are compared, and differences in goals and in characteristics of these devices are enumerated.</style></abstract><work-type><style face="normal" font="default" size="100%">Research report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time-order error and scalar variance in a computational model of human timing: simulations and predictions</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Cognitive Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1186/s40469-015-0002-0</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">1–24</style></pages><abstract><style face="normal" font="default" size="100%">This work introduces a computational model of human temporal discrimination mechanism - the Clock-Counter Timing Network. It is an artificial neural network implementation of a timing mechanism based on the informational architecture of the popular Scalar Timing Model. The model has been simulated in a virtual environment enabling computational experiments which imitate a temporal discrimination task - the two-alternative forced choice task. The influence of key parameters of the model (including the internal pacemaker speed and the variability of memory translation) on the network accuracy and the time-order error phenomenon has been evaluated. The results of simulations reveal how activities of different modules contribute to the overall performance of the model. These results can be compared and verified in empirical experiments with human participants, especially when the modes of activity of the internal timing mechanism are changed because of some external conditions, or are impaired due to some kind of a neural degradation process.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author><author><style face="normal" font="default" size="100%">Dorota Leszczyńska-Jasion</style></author><author><style face="normal" font="default" size="100%">Mariusz Urbański</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying efficient abductive hypotheses using multi-criteria dominance relation</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Computational Logic</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/IdentifyingEfficientAbductiveHypotheses.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">28:1–28:20</style></pages><abstract><style face="normal" font="default" size="100%">In this article, results of the automation of an abductive procedure are reported. This work is a continuation of our earlier research, where a general scheme of the procedure has been proposed. Here, a more advanced system developed to generate and evaluate abductive hypotheses is introduced. Abductive hypotheses have been generated by the implementation of the Synthetic Tableau Method. Before the evaluation, the set of hypotheses has undergone several reduction phases. To assess usefulness of abductive hypotheses in the reduced set, several criteria have been employed. The evaluation of efficiency of the hypotheses has been provided by the multi-criteria dominance relation. To comprehend the abductive procedure and the evaluation process more extensively, analyses have been conducted on a number of artificially generated abductive problems.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Szymon Ulatowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel computing in Framsticks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/ParallelComputingFramsticks.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA-18/2013</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">This report demonstrates how parallel computation can be implemented in the Framsticks environment. A number of possible multithreaded and distributed architectures and configurations is shown. The main part of this report discusses and explains two experiment definitions (prime-mt and standard-mt) that exploit multithreading. These experiment definitions are included in the official Framsticks distribution. The first one serves as a minimal example of how parallelization can be implemented in Framsticks. The second one is more complex: it shows how to deal with Slave experiments that do not have an internal stop condition, how to migrate the evolved genotypes between Slaves, and how to use Slave checkpoint events to monitor the progress of evolution.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Postrzeganie czasu przez człowieka: specyfika, modele i symulacje</style></title><secondary-title><style face="normal" font="default" size="100%">Anestezjologia i Ratownictwo</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">błąd porządku czasowego</style></keyword><keyword><style  face="normal" font="default" size="100%">postrzeganie upływu czasu</style></keyword><keyword><style  face="normal" font="default" size="100%">sieć neuronowa</style></keyword><keyword><style  face="normal" font="default" size="100%">symulacja</style></keyword><keyword><style  face="normal" font="default" size="100%">TOE</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.akademiamedycyny.pl/wp-content/uploads/2016/05/201301_AiR_013.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Akademia Medycyny</style></publisher><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">113–124</style></pages><abstract><style face="normal" font="default" size="100%">Niniejszy artykuł poświęcony jest zagadnieniu postrzegania czasu trwania zdarzeń (ang. timing) przez człowieka. Zarysowano w nim bieżący stan wiedzy zwracając uwagę na niedostatek modeli obejmujących szeroki zakres dziedzin, w których prowadzi się badania postrzegania czasu. Przedstawiono przykładową implementację popularnego modelu typu zegar-licznik w środowisku sztucznych sieci neuronowych. Opisano zjawisko &quot;błędu porządku czasowego&quot; oraz pokazano, jak zaproponowana sztuczna sieć neuronowa może naśladować zachowanie człowieka popełniając podobne błędy w zależności od parametrów opisujących działanie jej elementów składowych. Artykuł omawia też rolę technik symulacyjnych oraz metod sztucznej inteligencji i sztucznego życia w budowaniu działających i weryfikowalnych modeli niepoznanych jeszcze procesów i zjawisk biologicznych, których przykładem jest postrzeganie czasu.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary design of tall structures</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/EvolutionaryDesignOfTallStructures.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring quantities using oscillators and pulse generators</style></title><secondary-title><style face="normal" font="default" size="100%">Theory in Biosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s12064-012-0153-4</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">131</style></volume><pages><style face="normal" font="default" size="100%">103–116</style></pages><abstract><style face="normal" font="default" size="100%">This article presents properties of the clock–counter model with a periodic generator employed as the source of regularly emitted pulses. The pacemaker and accumulator mechanisms are often considered in research in neurobiology and cognitive science: neurons or their groups serve as oscillators, and the number of spikes emitted while a stimulus lasts becomes an estimate of the length of the stimulus. The article integrates three approaches: a theoretical model to present the general concept, a working implementation of this model to perform intensive simulation experiments, and the analytical description of the behavior of the model. Oscillators that exhibit some degree of regularity have been compared to the Poisson ones, and the corresponding probability distributions have been presented that describe the number of pulses accumulated over time. Several continuous and discrete interpulse distributions have been investigated, and the influence of generator parameters on the possible outcomes of the measurement have been described. Particular attention has been paid to the relationship between measurement variability and the mean number of pulses observed. Issues concerning practical realizations of periodic generators: discrete time, dependence of the generator start time on the stimulus, and relation to Weber’s law have been discussed as well.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author><author><style face="normal" font="default" size="100%">Mariusz Urbanski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-criteria evaluation of abductive hypotheses: towards efficient optimization in proof theory</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 18th International Conference on Soft Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Brno, Czech Republic</style></pub-location><pages><style face="normal" font="default" size="100%">320–325</style></pages><abstract><style face="normal" font="default" size="100%">Research described in this paper aims at implementation of an abductive procedure based on the Synthetic Tableaux Method. This problem concerns a two-stage process of generation and evaluation of abductive hypotheses. While generation of the abductive hypotheses is achieved here by the implementation of the rules provided with a logical apparatus, their evaluation is performed using traditional measures and optimization techniques such as Pareto optimality and fitness–distance analysis. By combining tools known in proof theory and in Artificial Intelligence, we explore the intriguing problem of interpretative reasoning.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementation and Simulation of the Scalar Timing Model</style></title><secondary-title><style face="normal" font="default" size="100%">Bio-Algorithms and Med-Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://bit.wl.cm.uj.edu.pl/cm/uploads/2021/02/bams7_4.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">41–52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Marek Kubiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative measure of structural and geometric similarity of 3D morphologies</style></title><secondary-title><style face="normal" font="default" size="100%">Complexity</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_Kubiak_MeasureSimilarity3DMorphologies.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">Wiley</style></publisher><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">40–52</style></pages><abstract><style face="normal" font="default" size="100%">This work describes a new heuristic algorithm that estimates structural and geometric similarity of three-dimensional morphologies. It is an extension to previously developed measure of similarity (Komosinski et al., 2001) that was only able to consider the structure of 3D constructs. Morphologies are modeled as graphs with vertices as points in a 3D space, and edges connecting these vertices. This model is very general, therefore the proposed algorithm can be applied in (and across) a number of disciplines including artificial life, evolutionary design, engineering, robotics, biology and chemistry. The primary areas of application of this fast numerical similarity measure are artificial life and evolutionary design, where great numbers of morphologies result from simulated evolutionary processes, and both structural and geometric aspects are significant. Geometry of 3D constructs (i.e., locations of body parts in space) is as important as the structure (i.e., connections of body parts), because both determine behavior of creatures or designs and their fitness in a particular environment. In this work both morphological aspects are incorporated in a single, highly discriminative measure of similarity.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Życie w komputerze: symulacja czy rzeczywistość?</style></title><secondary-title><style face="normal" font="default" size="100%">Nauka</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.alife.pl/zycie-w-komputerze-symulacja-czy-rzeczywistosc</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">Polska Akademia Nauk</style></publisher><pub-location><style face="normal" font="default" size="100%">Warszawa</style></pub-location><volume><style face="normal" font="default" size="100%">2011</style></volume><pages><style face="normal" font="default" size="100%">83–93</style></pages><abstract><style face="normal" font="default" size="100%">Niniejszy artykuł przedstawia dwa podejścia do wykorzystania komputerów jako środowiska, w którym mogą przebiegać procesy życiowe. W pierwszym z nich komputer służy do symulacji modeli życia ziemskiego - zarówno tych dokładnych, umożliwiających lepsze poznanie określonych zjawisk, jak i modeli uproszczonych (takich jak sztuczne sieci neuronowe czy algorytmy ewolucyjne), interesujących z pragmatycznego punktu widzenia. Drugie z opisywanych podejść polega na stworzeniu w komputerze środowiska, które umożliwiłoby rozwój życia. Artykuł pokazuje przydatność pierwszego z tych podejść oraz wskazuje niektóre problemy, pytania i wątpliwości związane z realizacją drugiego z nich.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Krzysztof Rosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating similarity of neural network dynamics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/SimilarityNeuralNetworkDynamics.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA-10/10</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This report concerns estimation of the similarity between neural networks of any topology. Motivations and benefits of having an automated and quantitative network comparison mechanism are presented. The concept of neural network dynamics (neuron output signal) is considered. A measure is proposed for estimating similarity of active (i.e., working) neural networks. Properties of the measure are analyzed theoretically and verified empirically. The experiments have been performed on a set of evolved networks responsible for controlling 3D structures (agents, robots). These experiments demonstrate the capabilities and the limitations of the proposed measure as a mechanism to support humans in analyzing large sets of neural networks.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Jan Polak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolving free-form stick ski jumpers and their neural control systems</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Conference on Evolutionary Computation and Global Optimization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_Polak_EvolvedSkiJumping.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Poland</style></pub-location><pages><style face="normal" font="default" size="100%">103--110</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper concerns evolution of stick agents in a simplified ski-jumping task. Both body morphologies and control systems are optimized. Evolutionary processes are performed in a range of conditions: the air drag and the friction of the ramp varies. Qualitative and quantitative analyses are presented that show how jump distance, jump height, and flight trajectory depend on environmental conditions. Jumping and landing strategies are investigated, and the most interesting evolved behaviors are reported.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Szymon Ulatowski</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Andrew Adamatzky</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Framsticks: Creating and Understanding Complexity of Life</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life Models in Software</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springer.com/978-1-84882-284-9</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">second</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><pages><style face="normal" font="default" size="100%">107–148</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This chapter describes Framsticks, a three-dimensional life simulation project. Both mechanical structures (&quot;bodies&quot;) and control systems (&quot;brains&quot;) of creatures are modeled. It is possible to design various kinds of experiments in this environment, including simple optimization (by evolutionary algorithms), coevolution, open-ended and spontaneous evolution, distinct gene pools and populations, diverse genotype-phenotype mappings, and modeling of species and ecosystems. Framsticks is employed in evolutionary computation, artificial intelligence, neural networks, biology, robotics and simulation, cognitive science, neuroscience, medicine, philosophy, virtual reality, graphics, and art. It is a versatile tool for research and education.</style></abstract><section><style face="normal" font="default" size="100%">5</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Models and implementations of timing processes using Artificial Life techniques</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/HumanTimingModelsSimulations.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA-05/09</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">This work presents implementation of the Scalar Timing Model (STM) in the neural networks environment. STM is rather popular and commonly used model in the perception of time intervals in humans and animals fields of study. Currently many experiments are conducted in order to verify and research STM parameters and attributes. One of the goal of the implementation was to check whether theoretical model will cope with constraints of artificial neural networks. During implementation process it turned out, that scheme of the model should be revised (by adding extra components) in order to maintain it's functional adequacy. Another case was to check how does manipulations of certain parameters will influence collected representation of the real time within model. In this preliminary research we focus on the pacemaker module. Conclusion of this research is that appropriate choice of distribution form of impulses generated by pacemaker make it simulation of the model more congruent with the experimentally collected data then with formal assumptions of STM.</style></abstract><work-type><style face="normal" font="default" size="100%">Research report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Hapke</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary Design of Interpretable Fuzzy Controllers</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computing and Decision Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_EvolveInterpretableFuzzy.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">351–367</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents an approach that allows to evolve fuzzy controllers that can be expressed as fuzzy rules in human-readable form and interpreted. For comparison, the evolution is also performed on simple neural controllers. The control task considered here is a balancing problem, where a construct made of articulated elastic elements is equipped with sensors and actuators. The goal of the construct is to keep the top heavy part from touching the ground. Evolved controllers are evaluated using computer simulation. Control systems process signals from tilt sensors to actuators fixed in the construct. During evolution, fuzzy controllers (including their fuzzy sets and rules) are reconfigured by genetic operators in order to maximize fitness of the control. The article compares evolvability of neural and fuzzy controllers and demonstrates how additional, comprehensible knowledge can be gained which explains the work of the fuzzy controller. The representation for the fuzzy control system, evolutionary operators, various evaluation functions, and the best evolved control systems are presented. A sample evolved fuzzy control system is analyzed in detail to explain its behavior.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wojciech Jaskowski</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Numerical Measure of Symmetry for 3D Stick Creatures</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Fall</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/NumericalMeasureSymmetry3DCreatures.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">425–443</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work introduces a numerical, continuous measure of symmetry for 3D stick creatures and solid 3D objects. Background information about the property of symmetry is provided, and motivations to developing symmetry measure are described. Three approaches are mentioned, and two of them are presented in detail using a formal mathematical language. The best approach is used to sort a set of creatures according to their symmetry. Experiments with a mixed set of 84 individuals originating from both human design and evolution are performed to examine symmetry within these two sources, and to determine if human designers and evolutionary processes prefer symmetry or asymmetry.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Podstawowe parametry ruchu piórka w teście rysunkowym i ich zastosowanie w diagnostyce motoryki kończyn górnych</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.put.poznan.pl/mkomosinski/research/parkinson-ocena-drgan-tablet.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RB-04/08</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">W artykule przedstawiono sposoby wyznaczania pięciu podstawowych parametrów ruchu piórka, odzwierciedlających natężenie drgań w ruchu ręki. Ruch ręki rejestrowany jest za pomocą tabletu podłączonego do komputera. Dane wykorzystane do eksperymentów pochodzą z badań pacjentów cierpiących na chorobę Parkinsona, którzy zostali poddani zabiegom palidotomii i talamotomii. W opisanych eksperymentach rozważa się cały zapis ruchu piórka (tzn. cały test rysunkowy), bez podziału na poszczególne elementy rysunku lub etapy rysowania. Pomimo tego, że algorytmy wyznaczające podstawowe parametry ruchu są stosunkowo proste, uśrednione wartości tych parametrów pozwalają odróżnić pacjentów przed i po zabiegu, a także określić rodzaj przeprowadzonej operacji.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sztuczne życie. Algorytmy inspirowane biologicznie</style></title><secondary-title><style face="normal" font="default" size="100%">Nauka</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.alife.pl/czym-jest-sztuczne-zycie</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">Polska Akademia Nauk</style></publisher><pub-location><style face="normal" font="default" size="100%">Warszawa</style></pub-location><volume><style face="normal" font="default" size="100%">2008</style></volume><pages><style face="normal" font="default" size="100%">7–21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wojciech Jaskowski</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring symmetry of moving stick creatures</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Marek Kubiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing dynamics of evolutionary optimization processes in spaces of reduced dimensionality</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">RA-023/05</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Marek Kubiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aspekt geometrii w szacowaniu podobieństwa wirtualnych organizmów</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">RB-002/04</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Szymon Ulatowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic mappings in artificial genomes</style></title><secondary-title><style face="normal" font="default" size="100%">Theory in Biosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/GeneticMappingsInArtificialGenomes.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">123</style></volume><pages><style face="normal" font="default" size="100%">125–137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper concerns processing of genomes of artificial (computer-simulated) organisms. Of special interest is the process of translation of genotypes into phenotypes, and utilizing the mapping information obtained during such translation. If there exists more than one genetic encoding in a single artificial life model, then the translation may also occur between different encodings. The obtained mapping information allows to present genes-phenes relationships visually and interactively to a person, in order to increase understanding of the genotype-to-phenotype translation process and genetic encoding properties. As the mapping associates parts of the source sequence with the translated destination, it may be also used to trace genes, phenes, and their relationships during simulated evolution. 

A mappings composition procedure is formally described, and a simple method of visual mapping presentation is established. Finally, advanced visualizations of gene-phene relationships are demonstrated as practical examples of introduced techniques. These visualizations concern genotypes expressed in various encodings, including an encoding which exhibits polygenic and pleiotropic properties.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Hapke</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Dawid Waclawski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of Evolutionarily Optimized Fuzzy Controllers for Virtual Robots</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 7th Joint Conference on Information Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_FuzzyControl_CINC2003.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Intelligent Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">North Carolina, USA</style></pub-location><pages><style face="normal" font="default" size="100%">1605–1608</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Hapke</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Dawid Waclawski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary optimization of fuzzy controllers for virtual robots</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">EA</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy</style></keyword><keyword><style  face="normal" font="default" size="100%">Robotics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">RA-010/02</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Rotaru-Varga</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of different genotype encodings for simulated 3D agents</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">AL</style></keyword><keyword><style  face="normal" font="default" size="100%">Body and Brain evol.</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Theory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Fall</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/ComparisonGeneticEncodings3DAgents.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Cambridge, MA</style></pub-location><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">395–418</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper analyzes the effect of different genetic encodings used for evolving 3D agents with physical morphologies. The complex phenotypes used in such systems often require nontrivial encodings. Different encodings used in Framsticks - a system for evolving 3D agents - are presented. These include a low-level direct mapping and two higher-level encodings: a recurrent and a developmental one. Quantitative results are presented from three simple optimization tasks (active height, passive height, and locomotion speed). The low-level encoding produced solutions of lower fitness than the two higher-level encodings under similar conditions. Results from recurrent and developmental encodings had similar fitness values but displayed qualitative differences. Desirable advantages and some drawbacks of more complex encodings are established.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Grzegorz Koczyk</style></author><author><style face="normal" font="default" size="100%">Marek Kubiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On estimating similarity of artificial and real organisms</style></title><secondary-title><style face="normal" font="default" size="100%">Theory in Biosciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AL</style></keyword><keyword><style  face="normal" font="default" size="100%">Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">EA</style></keyword><keyword><style  face="normal" font="default" size="100%">Theory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_Similarity_TheoryInBiosc2001.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3-4</style></number><volume><style face="normal" font="default" size="100%">120</style></volume><pages><style face="normal" font="default" size="100%">271–286</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Jerzy Blaszczynski</style></author><author><style face="normal" font="default" size="100%">Marcin Szymanski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ewolucyjna optymalizacja fraz muzycznych sterowana przez człowieka</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AL</style></keyword><keyword><style  face="normal" font="default" size="100%">EA</style></keyword><keyword><style  face="normal" font="default" size="100%">Philosophy</style></keyword><keyword><style  face="normal" font="default" size="100%">Psychology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">RB-008/01</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Krzysztof Krawiec</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary weighting of image features for diagnosing of CNS tumors</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence In Medicine, Special Issue on Evolutionary Computation in Medicine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CI</style></keyword><keyword><style  face="normal" font="default" size="100%">EA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.cs.put.poznan.pl/mkomosinski/research/evolutionary-weighting-image-features-for-diagnosing-CNS-tumors.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">25–38</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Roman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary weighting of features for classification</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CI</style></keyword><keyword><style  face="normal" font="default" size="100%">EA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><number><style face="normal" font="default" size="100%">RA-007/99</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Szymon Ulatowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Framsticks: sztuczne życie – złożona symulacja stworzeń i ich ewolucji</style></title><secondary-title><style face="normal" font="default" size="100%">Materiały konferencyjne III Krajowej Konferencji Algorytmy Ewolucyjne i Optymalizacja Globalna KAEiOG (Proceedings of the National Conference on Evolutionary Computation and Global Optimization)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">AI</style></keyword><keyword><style  face="normal" font="default" size="100%">AL</style></keyword><keyword><style  face="normal" font="default" size="100%">Simulation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/Komosinski_Framsticks_KAEiOG1999.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Potok Złoty</style></pub-location><pages><style face="normal" font="default" size="100%">157–166</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>