Abstract
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.
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 language | English |
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Title of host publication | GECCO '22 |
Subtitle of host publication | Proceedings of the Genetic and Evolutionary Computation Conference |
Editors | Jonathan E. Fieldsend |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1417-1425 |
Number of pages | 9 |
ISBN (Electronic) | 9781450392372 |
DOIs | |
Publication status | Published - 8 Jul 2022 |
Event | GECCO '22: Genetic and Evolutionary Computation Conference - Boston, United States Duration: 9 Jul 2022 → 13 Jul 2022 |
Publication series
Name | GECCO: Genetic and Evolutionary Computation Conference |
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Conference
Conference | GECCO '22: Genetic and Evolutionary Computation Conference |
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Abbreviated title | GECCO 2022 |
Country/Territory | United States |
City | Boston |
Period | 9/07/22 → 13/07/22 |
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.
Keywords
- Evolutionary algorithms
- self-adaptation
- multi-modal functions
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Theoretical Computer Science