Automatic adaptation of hypermutation rates for multimodal optimisation

Dogan Corus, Pietro S. Oliveto, Donya Yazdani

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

10 Citations (SciVal)
85 Downloads (Pure)

Abstract

Previous work has shown that in Artificial Immune Systems (AIS) the best static mutation rates to escape local optima with the ageing operator are far from the optimal ones to do so via large hyper-mutations and vice-versa. In this paper we propose an AIS that automatically adapts the mutation rate during the run to make good use of both operators. We perform rigorous time complexity analyses for standard multimodal benchmark functions with significant characteristics and prove that our proposed algorithm can learn to adapt the mutation rate appropriately such that both ageing and hypermutation are effective when they are most useful for escaping local optima. In particular, the algorithm provably adapts the mutation rate such that it is efficient for the problems where either operator has been proven to be effective in the literature.

Original languageEnglish
Title of host publicationFOGA 2021 - Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450383523
DOIs
Publication statusPublished - 06 Sept 2021
Event16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2021 - Virtual, Online, Austria
Duration: 06 Sept 202108 Sept 2021

Publication series

NameFOGA 2021 - Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms

Conference

Conference16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2021
Country/TerritoryAustria
CityVirtual, Online
Period06 Sept 202108 Sept 2021

Keywords

  • ageing
  • artificial immune systems
  • evolutionary algorithms
  • hypermutations
  • multimodal optimization
  • parameter adaptation
  • randomized search heuristics
  • runtime analysis

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