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Basic aspects of evolution strategies

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Abstract

Evolution strategies (ESs) are a special class of probabilistic, direct, global optimization methods. They are similar to genetic algorithms but work in continuous spaces and have the additional capability of self-adapting their major strategy parameters. This paper presents the most important features of ESs, namely their self-adaptation, as well as their robustness and potential for parallelization which they share with other evolutionary algorithms.

Besides the early (1 + 1)-ES and its underlying theoretical results, the modern (μ + λ)-ES and (μ, λ)-ES are presented with special emphasis on the self-adaptation of strategy parameters, a mechanism which enables the algorithm to evolve not only the object variables but also the characteristics of the probability distributions of normally distributed mutations. The self-adaptation property of the algorithm is also illustrated by an experimental example.

The robustness of ESs is demonstrated for noisy fitness evaluations and by its application to discrete optimization problems, namely the travelling salesman problem (TSP).

Finally, the paper concludes by summarizing existing work and general possibilities regarding the parallelization of evolution strategies and evolutionary algorithms in general.

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Authors and Affiliations

  1. Department of Computer Science, University of Dortmund, 44221, Dortmund, Germany

    Thomas Bäck

  2. EUnet Deutschland GmbH, Emil-Figge-Str. 80, 44227, Dortmund, Germany

    Frank Hoffmeister

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  1. Thomas Bäck
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Bäck, T., Hoffmeister, F. Basic aspects of evolution strategies. Stat Comput 4, 51–63 (1994). https://doi.org/10.1007/BF00175353

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