@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 {2561,
	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 {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}
}
@conference {2557,
	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 {2558,
	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 {2553,
	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 {2344,
	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 {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}
}
@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 Topa, Pawe{\l} and Jaros{\l}aw Tyszka},
	editor = {Gruca, Aleksandra and Brachman, Agnieszka and Kozielski, Stanis{\l}aw and Czach{\'o}rski, Tadeusz}
}
@conference {gajda2016connectionist,
	title = {A connectionist approach to abductive problems: employing a learning algorithm},
	booktitle = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)},
	year = {2016},
	pages = {353{\textendash}362},
	publisher = {ACSIS},
	organization = {ACSIS},
	abstract = {This paper presents preliminary results of an application of artificial neural networks and Backpropagation learning algorithm to solve logical abductive problems. To represent logic programs in the form of artificial neural networks CIL2P approach proposed by Garcez et al. is employed. Our abductive procedure makes use of translation of a logic program representing a knowledge base into a neural network, training of the neural network with an example representing an abductive goal and translation of the trained network back to the form of a logic program. An abductive hypothesis is represented as the symmetric difference between the initial logic program and the one obtained after training of the network. The first part of the paper introduces formal description of the tools used to model the abductive process, while the second part illustrates our contribution with results of a few computational experiments and discusses the ways of possible improvements of the proposed procedure.},
	doi = {10.15439/2016F484},
	author = {Andrzej Gajda and Adam Kups and Mariusz Urba{\'n}ski},
	editor = {M. Ganzha and L. Maciaszek and M. Paprzycki}
}
@booklet {Foraminifera-framsticks,
	title = {Foraminifera: genetics, morphology, simulation, evolution},
	year = {2014},
	url = {http://www.framsticks.com/foraminifera},
	author = {Maciej Komosinski and Agnieszka Mensfelt and Topa, Pawe{\l} and Jaros{\l}aw Tyszka and Szymon Ulatowski}
}
@article {mandik2003vre,
	title = {Varieties of Representation in Evolved and Embodied Neural Networks},
	journal = {Biology and Philosophy},
	volume = {18},
	number = {1},
	year = {2003},
	pages = {95{\textendash}130},
	publisher = {Springer},
	url = {http://www.framsticks.com/files/common/Mandik_RepresentationsInNeuralNetworks.pdf},
	author = {Pete Mandik}
}
@article {mandik2002sn,
	title = {Synthetic Neuroethology},
	journal = {Metaphilosophy},
	volume = {33},
	number = {1\&2},
	year = {2002},
	pages = {11{\textendash}29},
	publisher = {Blackwell Synergy},
	url = {http://www.petemandik.com/philosophy/papers/synthneur.pdf},
	author = {Pete Mandik}
}
@book {Komosinski-and-Rotaru-Varga-2000,
	title = {From Directed to Open-Ended Evolution in a Complex Simulation Model},
	series = {Artificial Life VII},
	year = {2000},
	pages = {293{\textendash}299},
	publisher = {MIT Press},
	organization = {MIT Press},
	keywords = {Agents, AL, Simulation, Theory},
	author = {Maciej Komosinski and Adam Rotaru-Varga},
	editor = {Mark A. Bedau and John S. McCaskill and Norman H. Packard and Steen Rasmussen}
}
@book {Komosinski-and-Ulatowski-1999b,
	title = {Framsticks: towards a simulation of a nature-like world, creatures and evolution},
	series = {Advances in Artificial Life. Lecture Notes in Artificial Intelligence 1674},
	year = {1999},
	pages = {261{\textendash}265},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	doi = {10.1007/3-540-48304-7_33},
	url = {http://www.framsticks.com/files/common/Komosinski_FramsticksEvol_ECAL1999.pdf},
	author = {Maciej Komosinski and Szymon Ulatowski},
	editor = {Dario Floreano and Jean-Daniel Nicoud and Francesco Mondada}
}
