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An enhanced adaptive geometry evolutionary algorithm using stochastic diversity mechanism

Abstract : Evolutionary Algorithms have been regularly used for solving multi and many objectives optimization problems. The effectiveness of such methods is determined generally by their ability to generate a well-distributed front (diversity) that is as close as possible to the optimal Pareto front (proximity). Analysis of current multiobjective evolutionary frameworks shows that they are still suboptimal and present poor versatility on different geometries and dimensionalities. For that, in this paper, we present AGE-MOEA++, a new Multi and Many Objective Evolutionary Algorithm that: (1) incorporates the principle of Pareto Front (PF) shape fitting to enhance the convergence in different shaped high dimensional objective spaces, and (2) adapts K-means ++ fundamentals in order to best manage the diversity in non-uniform distributed PF. The empirical study shows that our proposal has better results than the state-of-the-art approaches in terms of IGD and is competitive in terms of GD.
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https://hal.archives-ouvertes.fr/hal-03726431
Contributor : Cyril DE RUNZ Connect in order to contact the contributor
Submitted on : Monday, July 18, 2022 - 3:05:41 PM
Last modification on : Saturday, July 23, 2022 - 3:44:51 AM

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Fodil Benali, Damien Bodénès, Cyril de Runz, Nicolas Labroche. An enhanced adaptive geometry evolutionary algorithm using stochastic diversity mechanism. Genetic and Evolutionary Computation Conference (GECCO 2022), Jul 2022, Boston, United States. pp.476-483, ⟨10.1145/3512290.3528820⟩. ⟨hal-03726431⟩

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