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Communication Dans Un Congrès Année : 2022

The (1 + (λ, λ)) global SEMO algorithm

Résumé

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).
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Dates et versions

hal-03806399 , version 1 (07-10-2022)
hal-03806399 , version 2 (30-03-2023)

Identifiants

Citer

Benjamin Doerr, Omar El Hadri, Adrien Pinard. The (1 + (λ, λ)) global SEMO algorithm. GECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston Massachusetts, United States. pp.520-528, ⟨10.1145/3512290.3528868⟩. ⟨hal-03806399v2⟩
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