Towards Multi-objective Mixed Integer Evolution Strategies

Author(s):
Blom, K. van der (0000-0002-4653-0707); Yang, K. (0000-0002-1753-6464); Bäck, T.; Emmerich, M.T.M. (0000-0002-7342-2090)
Editor(s):
Deutz, A.H.; Hille, S.C.; Sergeyev, Y.D.
Published
2019
Publication type
Article in monograph or in proceedings

Abstract

Many problems are of a mixed integer nature, rather than being

restricted to a single variable type. Although mixed integer algorithms

exist for the single-objective case, work on the multi-objective case

remains limited. Evolution strategies are stochastic optimisation algorithms that feature step size adaptation mechanisms and are typically used in continuous domains. More recently they were generalised to mixed integer problems. In this work, first steps are taken towards extending the single-objective mixed integer evolution strategy for the multi-objective case. First results are promising, but step size adaptation for the multi-objective case can likely be improved.

Date issued
2019-02-12
Language
English
Publisher
AIP Publishing
Is part of
Proceedings LeGO - 14th International Global Optimization Workshop (2019), Pt. 2070, p. 020046
hdl
1887/69874
doi
10.1063/1.5090013

Conference

Meeting name
LeGO - 14th International Global Optimization Workshop
Meeting location
Leiden, Netherlands
Meeting date
2018-09-18 - 2018-09-21