Improving sampling in evolution strategies through mixture-based distributions built from past problem instances

Stephen FRIESS*, Peter TIŇO, Stefan MENZEL, Bernhard SENDHOFF, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

5 Citations (Scopus)


The notion of learning from different problem instances, although an old and known one, has in recent years regained popularity within the optimization community. Notable endeavors have been drawing inspiration from machine learning methods as a means for algorithm selection and solution transfer. However, surprisingly approaches which are centered around internal sampling models have not been revisited. Even though notable algorithms have been established in the last decades. In this work, we progress along this direction by investigating a method that allows us to learn an evolutionary search strategy reflecting rough characteristics of a fitness landscape. This latter model of a search strategy is represented through a flexible mixture-based distribution, which can subsequently be transferred and adapted for similar problems of interest. We validate this approach in two series of experiments in which we first demonstrate the efficacy of the recovered distributions and subsequently investigate the transfer with a systematic from the literature to generate benchmarking scenarios. © The Author(s) 2020.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I
EditorsThomas BÄCK, Mike PREUSS, André DEUTZ, Hao WANG, Carola DOERR, Michael EMMERICH, Heike TRAUTMANN
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Electronic)9783030581121
ISBN (Print)9783030581114
Publication statusPublished - 2020
Externally publishedYes
EventInternational Conference on Parallel Problem Solving from Nature 2020 - Leiden, Netherlands
Duration: 5 Sept 20209 Sept 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


ConferenceInternational Conference on Parallel Problem Solving from Nature 2020
Abbreviated titlePPSN 2020

Bibliographical note

This research has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement number 766186. It was also supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).


  • Algorithm configuration
  • Continuous optimisation
  • Evolution strategies
  • Model-based optimisation
  • Transfer learning


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