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Communication Dans Un Congrès Année : 2022

Stagnation Detection meets Fast Mutation

Résumé

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution ("fast mutation", Doerr et al. (2017)) and increasing the mutation strength based on stagnation detection (Rajabi and Witt (2020)). Whereas the latter can obtain the asymptotically best probability of finding a single desired solution in a given distance, the former is more robust and performs much better when many improving solutions in some distance exist. In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution. However, when several improving solutions exist, it can outperform both the stagnationdetection approach and fast mutation. The new operator is more than an interleaving of the two previous mechanisms and it also outperforms any such interleaving.
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Dates et versions

hal-03797608 , version 1 (04-10-2022)
hal-03797608 , version 2 (30-03-2023)

Identifiants

Citer

Benjamin Doerr, Amirhossein Rajabi. Stagnation Detection meets Fast Mutation. Evolutionary Computation in Combinatorial Optimization (EvoCOP 2022), Apr 2022, Madrid, Spain. ⟨10.1007/978-3-031-04148-8_13⟩. ⟨hal-03797608v2⟩
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