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

2014/09/26 by Aguirre, Hernan, Liefooghe, Arnaud, Verel, Sébastien +1
#FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.1409.7478

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.

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