What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms

Genetic and Evolutionary Computation Conference (GECCO), pp. 787–795

Abstract

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.

Authors 5

1. Furong Ye

   Chinese Academy of Sciences

   Affiliation as printed

   Chinese Academy of Sciences, Beijing, China

2. Frank Neumann

   The University of Adelaide

   Affiliation as printed

   The University of Adelaide, Adelaide, Australia

3. Jacob de Nobel Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

4. Aneta Neumann

   The University of Adelaide

   Affiliation as printed

   The University of Adelaide, Adelaide, Australia

5. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands