@conference {130,
	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 {128,
	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}
}
