@booklet {112,
	title = {Cz{\l}owiecze{\'n}stw0},
	year = {2019},
	url = {http://www.mooncoder.com/czlowieczenstw0},
	author = {Maciej Komosinski}
}
@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 {Komosinski-and-Rotaru-Varga-2001,
	title = {Comparison of different genotype encodings for simulated 3D agents},
	journal = {Artificial Life Journal},
	volume = {7},
	number = {4},
	year = {2001},
	month = {Fall},
	pages = {395{\textendash}418},
	publisher = {MIT Press},
	address = {Cambridge, MA},
	abstract = {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.},
	keywords = {Agents, AL, Body and Brain evol., Genetics, Simulation, Theory},
	doi = {10.1162/106454601317297022},
	url = {http://www.framsticks.com/files/common/ComparisonGeneticEncodings3DAgents.pdf},
	author = {Maciej Komosinski and Adam Rotaru-Varga}
}
