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Introduction

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Stable Mutations for Evolutionary Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 797))

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Abstract

Practically any engineering activity, be it design, construction, modeling, control, etc., sooner or later leads to the necessity of solving a set of optimization problems. In practice, these problems usually appear to be multi-modal, sometimes multi-criteria or non-stationary (changing during the searching process). Therefore, standard optimization methods applied to solve them is inefficient. These techniques are usually based on the so-called hard selection—new base points for further searching space exploration are selected from the best points previously obtained. Using such procedures, the solution sequence is usually trapped near to the first found local extremum, without any possibility to localize others. So, the possibility of finding a global optimum is strongly limited in this case. Methods, known from the literature, which try to overcome this limitation can be divided into two classes: enumerative and stochastic ones.

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Authors and Affiliations

  1. Faculty of Computer Science, Electrical and Control Engineering, Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Andrzej Obuchowicz

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Obuchowicz, A. (2019). Introduction. In: Stable Mutations for Evolutionary Algorithms. Studies in Computational Intelligence, vol 797. Springer, Cham. https://doi.org/10.1007/978-3-030-01548-0_1

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