Self-adaptation via multi-objectivisation: a theoretical study

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The exploration vs exploitation dilemma is to balance exploring new but potentially less fit regions of the fitness landscape while also focusing on regions near the fittest individuals. For the tunable problem class SparseLocalOpt, a non-elitist EA with tournament selection can limit the percentage of “sparse” local optimal individuals in the population using a sufficiently high mutation rate (Dang et al., 2021). However, the performance of the EA depends critically on choosing the “right” mutation rate, which is problem instance-specific. A promising approach is self-adaptation, where parameter settings are encoded in chromosomes and evolved.

We propose a new self-adaptive EA for single-objective optimisation, which treats parameter control from the perspective of multi- objective optimisation: The algorithm simultaneously maximises the fitness and the mutation rates. Since individuals in “dense” fitness valleys survive high mutation rates, and individuals on “sparse” local optima only survive with lower mutation rates, they can co-exist on a non-dominated Pareto front.

Runtime analyses show that this new algorithm (MOSA-EA) can efficiently escape a local optimum with unknown sparsity, where some fixed mutation rate EAs become trapped. Complementary experimental results show that the MOSA-EA outperforms a range of EAs on random NK-Landscape and k-Sat instances.
Original languageEnglish
Title of host publicationGECCO '22
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference
EditorsJonathan E. Fieldsend
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450392372
Publication statusPublished - 8 Jul 2022
EventGECCO '22: Genetic and Evolutionary Computation Conference - Boston, United States
Duration: 9 Jul 202213 Jul 2022


ConferenceGECCO '22: Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by a Turing AI Fellowship (EPSRC grant ref EP/V025562/1). The computations were performed using University of Birmingham’s BlueBEAR and Baskerville HPC service.

Publisher Copyright:
© 2022 ACM.


  • Evolutionary algorithms
  • self-adaptation
  • multi-modal functions

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Theoretical Computer Science


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