An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization

Hernán Aguirre¹, Arnaud Liefooghe², Sébastien Verel³, and Kiyoshi Tanaka¹

¹Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano, 380-8553, Japan
ahernan@shinshu-u.ac.jp
ktanaka@shinshu-u.ac.jp

²Université Lille 1 LIFL, UMR CNRS 8022, Inria Lille-Nord Europe, France
arnaud.liefooghe@lifl.fr

³Université du Littoral Côte d’Opale, LISIC, 62228, Calais, France
verel@univ-littoral.fr

Abstract. This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.

LNCS 8672, p. 487 ff.

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