Abstract
Recent research on evolutionary multiobjective optimization has mainly focused on Pareto fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto sets as well. We do so on a simple, configurable problem, and detect interesting interactions between induced changes to the Pareto set and the ability of three optimization algorithms to keep track of Pareto fronts. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Preuss, M., Naujoks, B., & Rudolph, G. (2006). Pareto set and EMOA behavior for simple multimodal multiobjective functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4193 LNCS, pp. 513–522). Springer Verlag. https://doi.org/10.1007/11844297_52
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