Towards multi-objective mixed integer evolution strategies

Koen Van Der Blom, Kaifeng Yang, Thomas Bäck, Michael Emmerich

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings LeGO 2018 � 14th International Global Optimization Workshop
EditorsAndre H. Deutz, Sander C. Hille, Yaroslav D. Sergeyev, Michael T. M. Emmerich
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417984
Publication statusPublished - 12 Feb 2019
Event14th International Global Optimization Workshop, LeGO 2018 - Leiden, Netherlands
Duration: 18 Sept 201821 Sept 2018

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616


Conference14th International Global Optimization Workshop, LeGO 2018


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