@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}
}
@inbook {Komosinski-and-Ulatowski-2009,
	title = {Framsticks: Creating and Understanding Complexity of Life},
	booktitle = {Artificial Life Models in Software},
	year = {2009},
	pages = {107{\textendash}148},
	publisher = {Springer},
	organization = {Springer},
	edition = {second},
	chapter = {5},
	address = {New York},
	abstract = {This chapter describes Framsticks, a three-dimensional life simulation project. Both mechanical structures ("bodies") and control systems ("brains") of creatures are modeled. It is possible to design various kinds of experiments in this environment, including simple optimization (by evolutionary algorithms), coevolution, open-ended and spontaneous evolution, distinct gene pools and populations, diverse genotype-phenotype mappings, and modeling of species and ecosystems. Framsticks is employed in evolutionary computation, artificial intelligence, neural networks, biology, robotics and simulation, cognitive science, neuroscience, medicine, philosophy, virtual reality, graphics, and art. It is a versatile tool for research and education.},
	url = {http://www.springer.com/978-1-84882-284-9},
	author = {Maciej Komosinski and Szymon Ulatowski},
	editor = {Maciej Komosinski and Andrew Adamatzky}
}
@conference {Komosinski-and-Ulatowski-1999a,
	title = {Framsticks: sztuczne {\.z}ycie {\textendash} z{\l}o{\.z}ona symulacja stworze{\'n} i ich ewolucji},
	booktitle = {Materia{\l}y konferencyjne III Krajowej Konferencji Algorytmy Ewolucyjne i Optymalizacja Globalna KAEiOG (Proceedings of the National Conference on Evolutionary Computation and Global Optimization)},
	year = {1999},
	month = {May},
	pages = {157{\textendash}166},
	address = {Potok Z{\l}oty},
	keywords = {Agents, AI, AL, Simulation},
	url = {http://www.framsticks.com/files/common/Komosinski_Framsticks_KAEiOG1999.pdf},
	author = {Maciej Komosinski and Szymon Ulatowski}
}
