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The $(1+(λ,λ))$ Global SEMO Algorithm
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- The $(1+(λ,λ))$ genetic algorithm is a recently proposed single-objective evolutionary algorithm with several interesting properties. We show that its main working principle, mutation with a high rate and crossover as repair mechanism, can be transported also to multi-objective evolutionary computation. We define the $(1+(λ,λ))$ global SEMO algorithm, a variant of the classic global SEMO algorithm, and prove that it optimizes the OneMinMax benchmark asymptotically faster than the global SEMO. Following the single-objective example, we design a one-fifth rule inspired dynamic parameter setting (to the best of our knowledge for the first time in discrete multi-objective optimization) and prove that it further improves the runtime to $O(n^2)$, whereas the best runtime guarantee for the global SEMO is only $O(n^2 \log n)$.<br />Author generated version of a paper at GECCO 2022
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....1ba2a47ef0c5e40e6b7f62bc2abbe32a
- Full Text :
- https://doi.org/10.48550/arxiv.2210.03618