Optimising Optimisers with Push GP

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

Abstract

This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same problem, and on other continuous-valued problems. In most cases, the optimisers generalise well to the larger problems. Surprisingly, some of them also generalise very well to previously unseen problems, outperforming existing general purpose optimisers such as CMA-ES. Analysis of the behaviour of the evolved optimisers indicates a range of interesting optimisation strategies that are not found within conventional optimisers, suggesting that this approach could be useful for discovering novel and effective forms of optimisation in an automated manner.
Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publicationEuroGP 2020
PublisherSpringer
Pages101-117
Number of pages17
ISBN (Electronic)9783030440947
ISBN (Print)9783030440930
DOIs
Publication statusPublished - 9 Apr 2020
Event23rd European Conference on Genetic Programming 2020 - Seville, Spain
Duration: 15 Apr 202017 Apr 2020

Publication series

NameLecture Notes in Computer Science
Volume12101
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd European Conference on Genetic Programming 2020
Abbreviated titleEuroGP 2020
CountrySpain
CitySeville
Period15/04/2017/04/20

Keywords

  • Genetic Programming
  • Metaheuristics
  • Optimisation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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