@conference {2580,
	title = {GOM-Based Compatible Substitutions Optimization for Variable-Length Representation Gray-Box Problems},
	booktitle = {Genetic and Evolutionary Computation Conference (GECCO {\textquoteright}25 Companion)},
	year = {2025},
	publisher = {ACM},
	organization = {ACM},
	abstract = {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.},
	doi = {10.1145/3712255.3726717},
	url = {http://www.framsticks.com/files/common/GOM-BasedCompatibleSubstitutionsOptimization.pdf},
	author = {Maciej Komosinski and Konrad Miazga}
}
@conference {2579,
	title = {Late Bloomers, First Glances, Second Chances: Exploration of the Mechanisms Behind Fitness Diversity},
	booktitle = {Genetic and Evolutionary Computation Conference (GECCO {\textquoteright}24)},
	year = {2024},
	publisher = {ACM},
	organization = {ACM},
	abstract = {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.},
	doi = {10.1145/3638529.3654168},
	url = {http://www.framsticks.com/files/common/FitnessDiversityMechanismsInHFCAndConvectionSel.pdf},
	author = {Sofya Aksenyuk and Szymon Bujowski and Maciej Komosinski and Konrad Miazga}
}
@conference {2574,
	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 {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}
}
