Quasi-bistability of walk-based landscape measures in stochastic fitness landscapes

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

Exploratory landscape analysis is a useful method for algorithm selection, parametrization and creating an understanding of how a heuristic optimization algorithm performs on a problem and why. A prominent family of fitness landscape analysis measures are based on random walks through the search space. However, most of these features were only introduced on deterministic fitness functions and it is unclear, under which conditions walk-based landscape features are applicable to noisy optimization problems. In this paper, we empirically analyze the effects of noise in the fitness function on these measures and identify two dominant regimes, where either the underlying problem or the noise are described. Additionally, we observe how step sizes and walk lengths of random walks influence this behavior.

OriginalspracheEnglisch
TitelGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten1087-1094
Seitenumfang8
ISBN (elektronisch)9781450356183
DOIs
PublikationsstatusVeröffentlicht - 2 Jul 2018
Veranstaltung2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Dauer: 15 Jul 201819 Jul 2018

Publikationsreihe

NameGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

Konferenz

Konferenz2018 Genetic and Evolutionary Computation Conference, GECCO 2018
LandJapan
OrtKyoto
Zeitraum15.07.201819.07.2018

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