@inproceedings{cf6dde3c757e4dccba2c62cdcb5d0d7b,
title = "Improving sampling in evolution strategies through mixture-based distributions built from past problem instances",
abstract = "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. {\textcopyright} The Author(s) 2020.",
keywords = "Algorithm configuration, Continuous optimisation, Evolution strategies, Model-based optimisation, Transfer learning",
author = "Stephen FRIESS and Peter TI{\v N}O and Stefan MENZEL and Bernhard SENDHOFF and Xin YAO",
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).; International Conference on Parallel Problem Solving from Nature 2020, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020",
year = "2020",
doi = "10.1007/978-3-030-58112-1_40",
language = "English",
isbn = "9783030581114",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "583--596",
editor = "B{\"A}CK, {Thomas } and PREUSS, {Mike } and Andr{\'e} DEUTZ and WANG, {Hao } and DOERR, {Carola } and EMMERICH, {Michael } and TRAUTMANN, {Heike }",
booktitle = "Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I",
address = "Germany",
}