Abstract
Selection hyper-heuristics have been increasingly and successfully applied to numerical and discrete optimization problems. This paper proposes HHTS, a hyper-heuristic (HH) based on the Thompson Sampling (TS) mechanism to select combinations of low-level heuristics aiming to provide solutions for various continuous single-objective optimization benchmarks. Thompson Sampling is modeled in the present paper as a Beta Bernoulli sampler considering the increase/decrease of diversity among population individuals to measure the success/failure during the search. In the experiments, HHTS (a generic evolutionary algorithm generated by TS) is compared with five well-known evolutionary algorithms. Results indicate that, despite requiring less computational effort, HHTS's performance is similar or better than the other algorithm for most instances and in 50% of the cases it is capable of achieving the global optimum.
Originalsprog | Engelsk |
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Titel | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
Antal sider | 9 |
Forlag | Association for Computing Machinery |
Publikationsdato | 7 jul. 2021 |
Sider | 1394-1402 |
ISBN (Elektronisk) | 9781450383516 |
DOI | |
Status | Udgivet - 7 jul. 2021 |
Begivenhed | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, Frankrig Varighed: 10 jul. 2021 → 14 jul. 2021 |
Konference
Konference | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 |
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Land/Område | Frankrig |
By | Virtual, Online |
Periode | 10/07/2021 → 14/07/2021 |
Sponsor | ACM SIGEVO |
Navn | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
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Bibliografisk note
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