What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms
Furong Ye, Frank Neumann, Jacob de Nobel, Aneta Neumann, Thomas Bäck · Proceedings of the Genetic and Evolutionary Computation Conference · 2024
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algorithms for single-objective optimization, little is known about how self-adaptation influences multi-objective evolutionary algorithms. In this work, we contribute (1) extensive experimental analysis of the Global Simple Evolutionary Multi-objective Algorithm (GSEMO) variants on classic problems, such as OneMinMax, LOTZ, COCZ, and (2) a novel version of GSEMO with self-adjusting mutation rates.