Instruction-Level Design of Local Optimisers using Push GP

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

1 Citation (Scopus)
18 Downloads (Pure)

Abstract

This work uses genetic programming to explore the design space of local optimisation algorithms. Optimisers are expressed in the Push programming language, a stack-based language with a wide range of typed primitive instructions. The evolutionary framework provides the evolving optimisers with an outer loop and information about whether a solution has improved, but otherwise they are relatively unconstrained in how they explore optimisation landscapes. To test the utility of this approach, optimisers were evolved on four diferent types of continuous landscape, and the search behaviours of the evolved optimisers analysed. By making use of mathematical functions such as tangents and logarithms to explore diferent neighbourhoods, and also by learning features of the landscapes, it was observed that the evolved optimisers were often able to reach the optima using relatively short paths.

Original languageEnglish
Title of host publicationGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery
Pages1487-1494
Number of pages8
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
EventGenetic and Evolutionary Computation Conference 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Conference

ConferenceGenetic and Evolutionary Computation Conference 2019
Abbreviated titleGECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Automatic design
  • Continuous optimisation
  • Genetic programming
  • Hyperheuristics
  • Local optimisers
  • Metaheuristics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software

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  • Cite this

    Lones, M. A. (2019). Instruction-Level Design of Local Optimisers using Push GP. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1487-1494). Association for Computing Machinery. https://doi.org/10.1145/3319619.3326806