Cited by 4,397Metaheuristic Optimization Algorithms ResearchEvolutionary Algorithms and ApplicationsEvolution and Genetic Dynamics

Completely Derandomized Self-Adaptation in Evolution Strategies

Hansen, Nikolaus, Ostermeier, Andreas·2001·Evolutionary Computation

Abstract

This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

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Crossref · 10.1162/106365601750190398

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Authors
Hansen, N., & Ostermeier, A.
Affiliations
Technische Universität Berlin (DE)
Venue
Evolutionary Computation
Publisher
MIT Press
Published
2001-06
Volume
9
Issue
2
Pages
159-195
ISSN
  • 1063-6560 (ISSN-L)
  • 1530-9304
OpenAlex
W2112036188
Cited by
4,397
References
41
Citations by year
2026: 342 · 2025: 296 · 2024: 247 · 2023: 260 · 2022: 261 · 2021: 295 · 2020: 279 · 2019: 253 · 2018: 243 · 2017: 202 · 2016: 204 · 2015: 174 · 2014: 210 · 2013: 152 · 2012: 179
FWCI
31.27
Type
journal article
Language
en
Open access
closed
Locations
  • Evolutionary Computation (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
evolution strategies, self-adaptation, derandomization, covariance matrix adaptation, mutation distribution, cumulation, evolution path, global and local search
MeSH terms
Algorithms, Computer Simulation, Biological Evolution, Models, Genetic, Mutation, Stochastic Processes, Models, Statistical

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