@article {132,
	title = {Damage Prediction for Integrated DEAP and MRE Soft Actuators},
	journal = {Energies},
	volume = {17},
	year = {2024},
	pages = {2745},
	abstract = {Soft robotics is a hot scientific topic in areas such as medicine and medical care, implantology, haptic technologies, and the design of various flexible structures. Integrated actuators (DEAP and MRE) are characterized by special functionality and a wider range of operations than when used individually. Such actuators can later be controlled with high voltages ranging from several to a dozen or so kV. Unfortunately, the production process of integrated actuators is multi-stage and therefore more complicated. Thus, at the stage of prototyping, microscopic errors often occur that cannot be detected using simple measurement methods. The result of such errors is actuator damage at the testing stage or in subsequent application. Unfortunately, due to high voltages, actuator damage usually leads to it catching fire, which is potentially dangerous. This work presents an approach that enables the prediction of actuator damage at the testing stage. The results of modeling damaged actuators, a modified safe testing method, and a complete supervising system for testing the actuator with protection are shown. The work is also enriched with a set of data from the analyzed damage to DEAP and MRE actuators, which may prove useful in other research on the actuators of soft robotics.},
	issn = {1996-1073},
	doi = {10.3390/en17112745},
	author = {Bernat, Jakub and Ko{\l}ota, Jakub and Gajewski, Piotr and Marcinkowska, Agnieszka and Komosinski, Maciej and Szcz{\k e}sny, Szymon}
}
@conference {133,
	title = {Revealing the Inner Dynamics of Evolutionary Algorithms with Convection Selection},
	booktitle = {Genetic and Evolutionary Computation Conference Companion (GECCO {\textquoteright}23)},
	year = {2023},
	publisher = {ACM},
	organization = {ACM},
	address = {Lisbon, Portugal},
	abstract = {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.},
	doi = {10.1145/3583133.3590708},
	url = {http://www.framsticks.com/files/common/InnerDynamicsOfConvectionSelection.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@conference {130,
	title = {Diversification Techniques and Distance Measures in Evolutionary Design of 3D Structures},
	booktitle = {Genetic and Evolutionary Computation Conference Companion (GECCO {\textquoteright}22)},
	year = {2022},
	publisher = {ACM},
	organization = {ACM},
	address = {Boston, USA},
	abstract = {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.},
	doi = {10.1145/3520304.3528948},
	author = {Adam Klejda and Maciej Komosinski and Agnieszka Mensfelt}
}
@conference {131,
	title = {Fitness Diversification in the Service of Fitness Optimization: a Comparison Study},
	booktitle = {Genetic and Evolutionary Computation Conference Companion (GECCO {\textquoteright}22)},
	year = {2022},
	publisher = {ACM},
	organization = {ACM},
	address = {Boston, USA},
	abstract = {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.},
	doi = {10.1145/3520304.3528949},
	url = {http://www.framsticks.com/files/common/FitnessDiversity.pdf},
	author = {Kamil Basiukajc and Maciej Komosinski and Konrad Miazga}
}
@conference {127,
	title = {Automated development of latent representations for optimization of sequences using autoencoders},
	booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we propose an automated method for the development of new representations of sequences. For this purpose, we introduce a two-way mapping from variable length sequence representations to a latent representation modelled as the bottleneck of an LSTM (long short-term memory) autoencoder. Desirable properties of such mappings include smooth fitness landscapes for optimization problems and better evolvability. This work explores the capabilities of such latent encodings in the context of optimization of 3D structures. Various improvements are adopted that include manipulating the autoencoder architecture and its training procedure. The results of evolutionary algorithms that use different variants of automatically developed encodings are compared.},
	doi = {10.1109/CEC45853.2021.9504910},
	url = {http://www.framsticks.com/files/common/LatentRepresentationsForSequencesOptimization.pdf},
	author = {Kaszuba, Piotr and Komosinski, Maciej and Mensfelt, Agnieszka}
}
@conference {128,
	title = {Diversity control in evolution of movement},
	booktitle = {Artificial Life Conference Proceedings},
	year = {2021},
	publisher = {MIT Press},
	organization = {MIT Press},
	abstract = {In this work we investigate how various techniques of diversity control employed during evolution of 3D agents influence the velocity they achieve, and how these techniques influence the diversity of behaviors across multiple independent evolutionary runs. Three evolutionary settings are compared: a standard generational evolutionary process where fitness is velocity, a niching technique, and pure novelty search. Two genetic encodings (lower and higher level) and two environments (land and water) are used in experiments. To diversify behaviors, seven properties of movement introduced earlier are calculated for each individual during evolution. Best individuals obtained from evolution in each setting are compared both in terms of their fitness and the similarity of their movement patterns.},
	doi = {10.1162/isal_a_00456},
	author = {Komosinski, Maciej and Miazga, Konrad}
}
@article {120,
	title = {Human perception of similarity of 3D graph structures},
	year = {2020},
	abstract = {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.},
	issn = {RA-07/2020},
	author = {Maciej Komosinski and Agnieszka Mensfelt}
}
@conference {102,
	title = {A Flexible Dissimilarity Measure for Active and Passive 3D Structures and Its Application in the Fitness{\textendash}Distance Analysis},
	booktitle = {Applications of Evolutionary Computation},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Evolutionary design of 3D structures {\textendash} either static structures, or equipped with some sort of a control system {\textendash} is one of the hardest optimization tasks. One of the reasons are rugged fitness landscapes resulting from complex and non-obvious genetic representations of such structures and their genetic operators. This paper investigates global convexity of fitness landscapes in optimization tasks of maximizing velocity and height of both active and passive structures. For this purpose, a new dissimilarity measure for 3D active and passive structures represented as undirected graphs is introduced. The proposed measure is general and flexible {\textendash} any vertex properties can be easily incorporated as dissimilarity components. The new measure was compared against the previously introduced measure in terms of triangle inequality satisfiability, changes in raw measure values and the computational cost. The comparison revealed improvements for triangle inequality and raw values at the expense of increased computational complexity. The investigation of global convexity of the fitness landscape, involving the fitness{\textendash}distance correlation analysis, revealed negative correlation between the dissimilarity of the structures and their fitness for most of the investigated cases.},
	isbn = {978-3-030-16692-2},
	doi = {10.1007/978-3-030-16692-2_8},
	url = {http://www.framsticks.com/files/common/DissimilarityMeasure3DStructuresFitnessDistance.pdf},
	author = {Komosinski, Maciej and Mensfelt, Agnieszka},
	editor = {Kaufmann, Paul and Castillo, Pedro A.}
}
@article {111,
	title = {Mappism: formalizing classical and artificial life views on mind and consciousness},
	journal = {Foundations of Computing and Decision Sciences},
	volume = {44},
	year = {2019},
	pages = {55{\textendash}99},
	doi = {10.2478/fcds-2019-0005},
	url = {http://www.framsticks.com/files/common/MappismConsciousness.pdf},
	author = {Iwo B{\l}{\k a}dek and Maciej Komosinski and Konrad Miazga}
}
@inbook {115,
	title = {Measuring properties of movement in populations of evolved 3D agents},
	booktitle = {Artificial Life Conference Proceedings},
	year = {2019},
	pages = {485{\textendash}492},
	publisher = {MIT Press},
	organization = {MIT Press},
	doi = {10.1162/isal_a_00208},
	author = {Maciej Komosinski and Konrad Miazga},
	editor = {Harold Fellermann and Jaume Bacardit and Angel Goni-Moreno and Rudolf M. F{\"u}chslin}
}
@proceedings {103,
	title = {Parametrizing Convection Selection: Conclusions from the Analysis of Performance in the NKq Model},
	year = {2019},
	month = {2019},
	pages = {804{\textendash}811},
	publisher = {ACM},
	address = {Prague, Czech Republic},
	abstract = {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.},
	doi = {10.1145/3321707.3321864},
	url = {http://www.framsticks.com/files/common/ConvectionSelectionNKqModel.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@article {114,
	title = {Properties of movement of 3D agents},
	number = {RA-1/2019},
	year = {2019},
	institution = {Poznan University of Technology, Institute of Computing Science},
	url = {http://www.framsticks.com/files/common/PropertiesOfMovementOf3DAgents.pdf},
	author = {Krzysztof Gorgolewski and Maciej Komosinski and Konrad Miazga and Krzysztof Rosinski and Pawe{\l} Rych{\l}y}
}
@article {98,
	title = {Comparison of the tournament-based convection selection with the island model in evolutionary algorithms},
	journal = {Journal of Computational Science},
	volume = {32},
	year = {2018},
	pages = {106{\textendash}114},
	abstract = {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.},
	issn = {1877-7503},
	doi = {10.1016/j.jocs.2018.10.001},
	url = {http://www.framsticks.com/files/common/ConvectionSelectionVsIslandModel.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@article {109,
	title = {Tournament-based convection selection in evolutionary algorithms},
	journal = {PPAM 2017 proceedings, Lecture Notes in Computer Science},
	year = {2018},
	abstract = {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.},
	doi = {10.1007/978-3-319-78054-2_44},
	url = {http://www.framsticks.com/files/common/TournamentBasedConvectionSelectionEvolutionary.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@article {Komosinski-et-al-2016,
	title = {Multi-agent simulation of benthic foraminifera response to annual variability of feeding fluxes},
	journal = {Journal of Computational Science},
	volume = {21},
	year = {2017},
	pages = {419{\textendash}431},
	abstract = {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.},
	issn = {1877-7503},
	doi = {10.1016/j.jocs.2016.09.009},
	url = {http://www.framsticks.com/files/common/SimulationForaminiferaFeedingFluxes.pdf},
	author = {Maciej Komosinski and Agnieszka Mensfelt and Jaros{\l}aw Tyszka and Jan Gole{\'n}}
}
@inbook {Komosinski-et-al-2015,
	title = {Application of a morphological similarity measure to the analysis of shell morphogenesis in Foraminifera},
	booktitle = {Man{\textendash}Machine Interactions 4},
	series = {Advances in Intelligent Systems and Computing},
	volume = {391},
	year = {2016},
	pages = {215{\textendash}224},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	isbn = {978-3-319-23436-6},
	doi = {10.1007/978-3-319-23437-3_18},
	url = {http://www.framsticks.com/files/common/ForaminiferaGenotypePhenotypeMapping.pdf},
	author = {Maciej Komosinski and Agnieszka Mensfelt and Pawe{\l} Topa and Jaros{\l}aw Tyszka},
	editor = {Gruca, Aleksandra and Brachman, Agnieszka and Kozielski, Stanis{\l}aw and Czach{\'o}rski, Tadeusz}
}
@article {Komosinski-and-Mensfelt-2016,
	title = {Emotions perceived and emotions experienced in response to computer-generated music},
	journal = {Music Perception},
	volume = {33},
	number = {4},
	year = {2016},
	month = {April},
	pages = {432{\textendash}445},
	publisher = {UC Press},
	abstract = {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 {\textendash} the cues common to music and speech {\textendash} 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.},
	doi = {10.1525/MP.2016.33.4.432},
	url = {https://www.cs.put.poznan.pl/mkomosinski/research/music-emotions-perceived-and-experienced.pdf},
	author = {Maciej Komosinski and Agnieszka Mensfelt}
}
@inbook {108,
	title = {eVolutus: A New Platform for Evolutionary Experiments},
	booktitle = {Parallel Processing and Applied Mathematics: 11th International Conference, PPAM 2015, Krakow, Poland, September 6{\textendash}9, 2015. Revised Selected Papers, Part II},
	year = {2016},
	pages = {570{\textendash}580},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-319-32152-3},
	doi = {10.1007/978-3-319-32152-3_53},
	url = {http://dx.doi.org/10.1007/978-3-319-32152-3_53},
	author = {Topa, Pawe{\l} and Komosinski, Maciej and Tyszka, Jaros{\l}aw and Mensfelt, Agnieszka and Rokitta, Sebastian and Byrski, Aleksander and Bassara, Maciej},
	editor = {Wyrzykowski, Roman and Deelman, Ewa and Dongarra, Jack and Karczewski, Konrad and Kitowski, Jacek and Wiatr, Kazimierz}
}
