Stagnation Detection Meets Fast Mutation

Doerr, Benjamin; Rajabi, Amirhossein

doi:10.1007/978-3-031-04148-8_13

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…Taking this idea one step further gives the so-called fast (1 + 1) EA [6], sampling offspring at far distances significantly more frequently than the (1 + 1) EA, while still sampling search points at a distance of 1 with constant probability. Another idea is to adjust the search distance distribution whenever progress stagnates; this idea, so-called stagnation detection, was analyzed in [7,[21][22][23]. Note that it is typically fruitful to spend a lot of time searching the local neighborhood in order to exploit local structure.…”

Section: Discussionmentioning

confidence: 99%

Escaping Local Optima with Local Search: A Theory-Driven Discussion

Friedrich

Kötzing

Krejca

et al. 2022

Lecture Notes in Computer Science

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Local search is the most basic strategy in optimization settings when no specific problem knowledge is employed. While this strategy finds good solutions for certain optimization problems, it generally suffers from getting stuck in local optima. This stagnation can be avoided if local search is modified. Depending on the optimization landscape, different modifications vary in their success. We discuss several features of optimization landscapes and give analyses as examples for how they affect the performance of modifications of local search. We consider modifying random local search by restarting it and by considering larger search radii. The landscape features we analyze include the number of local optima, the distance between different optima, as well as the local landscape around a local optimum. For each feature, we show which modifications of local search handle them well and which do not.

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