@conference {2562,
	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}
}
@article {2342,
	title = {Mappism: formalizing classical and artificial life views on mind and consciousness},
	journal = {Foundations of Computing and Decision Sciences},
	volume = {44},
	number = {1},
	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 {2349,
	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}
}
@inbook {2350,
	title = {Parametrizing Convection Selection: Conclusions from the Analysis of Performance in the NKq Model},
	booktitle = {Genetic and Evolutionary Computation Conference (GECCO {\textquoteright}19), July 13{\textendash}17, 2019, Prague, Czech Republic},
	year = {2019},
	pages = {804{\textendash}811},
	publisher = {ACM},
	organization = {ACM},
	doi = {10.1145/3321707.3321864},
	url = {http://www.framsticks.com/files/common/ConvectionSelectionNKqModel.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@article {2348,
	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 {2343,
	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 {2347,
	title = {Tournament-based convection selection in evolutionary algorithms},
	journal = {PPAM 2017 proceedings, Lecture Notes in Computer Science},
	volume = {10778},
	year = {2018},
	pages = {466{\textendash}475},
	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}
}
