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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Colleges, School and Institutes

External organisations

  • Honda Research Institute Europe, Offenbach/Main
  • Honda Research Institute Europe GmbH


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.


Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XVI
Publication statusPublished - 31 Aug 2020
Event Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020. - Leiden, Netherlands
Duration: 5 Sep 20209 Sep 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020.


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