# How to Assess Step-Size Adaptation Mechanisms in Randomised Search

1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Step-size adaptation for randomised search algorithms like evolution strategies is a crucial feature for their performance. The adaptation must, depending on the situation, sustain a large diversity or entertain fast convergence to the desired optimum. The assessment of step-size adaptation mechanisms is therefore non-trivial and often done in too restricted scenarios, possibly only on the sphere function. This paper introduces a (minimal) methodology combined with a practical procedure to conduct a more thorough assessment of the overall population diversity of a randomised search algorithm in different scenarios. We illustrate the methodology on evolution strategies with $\sigma$-self-adaptation, cumulative step-size adaptation and two-point adaptation. For the latter, we introduce a variant that abstains from additional samples by constructing two particular individuals within the given population to decide on the step-size change. We find that results on the sphere function alone can be rather misleading to assess mechanisms to control overall population diversity. We observed the most striking flaws for self-adaptation: on the linear function, the step-size increments are rather small, and on a moderately conditioned ellipsoid function, the adapted step-size is 20 times smaller than optimal.
Type de document :
Communication dans un congrès
T. Bartz-Beielstein et al. Parallel Problem Solving from Nature, PPSN XIII, Sep 2014, Ljubljana, Slovenia. Springer, 8672, pp.60-69, 2014, LNCS. 〈10.1007/978-3-319-10762-2_6〉
Domaine :
Liste complète des métadonnées

https://hal.inria.fr/hal-00997294
Contributeur : Asma Atamna <>
Soumis le : jeudi 8 janvier 2015 - 15:04:45
Dernière modification le : jeudi 11 janvier 2018 - 01:49:38
Document(s) archivé(s) le : samedi 15 avril 2017 - 14:41:27

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ppsn2014assess.pdf
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### Citation

Nikolaus Hansen, Asma Atamna, Anne Auger. How to Assess Step-Size Adaptation Mechanisms in Randomised Search. T. Bartz-Beielstein et al. Parallel Problem Solving from Nature, PPSN XIII, Sep 2014, Ljubljana, Slovenia. Springer, 8672, pp.60-69, 2014, LNCS. 〈10.1007/978-3-319-10762-2_6〉. 〈hal-00997294v3〉

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