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Evolutionary Algorithms in Theory and PracticeEvolution Strategies, Evolutionary Programming, Genetic Algorithms$
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Thomas Bäck

Print publication date: 1996

Print ISBN-13: 9780195099713

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195099713.001.0001

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5 Selection
Evolutionary Algorithms in Theory and Practice

Thomas Bäck

Oxford University Press

The genetic operators summarized in the set Ω, i.e. mutation and recombination (and probably others, e.g. inversion) create new individuals in a completely undirected way. In Evolutionary Algorithms, the selection operator plays a major role by imposing a direction on the search process, i.e. a clear preference of those individuals which perform better according to the fitness measure Φ. Selection is the only component of Evolutionary Algorithms where the fitness of individuals has an impact on the evolution process. The practical implementations of selection as discussed in sections 2.1.4, 2.2.4, and 2.3.4 seemingly contradict the biological viewpoint presented in section 1.1, where natural selection was emphasized not to be an active force but instead to be characterized by different survival and reproduction rates. However, artificial implementation models and biological reality are not necessarily contradicting each other. While in biological systems fitness can only be measured indirectly by differences in growth rates, fitness in Evolutionary Algorithms is a direct, well-defined and evaluable property of individuals. The biological struggle for existence (e.g. by predator-prey interactions, capabilities of somatic adaptation, and the particular physical properties of individuals) has no counterpart in computer implementations of standard Evolutionary Algorithms. Therefore, an artificial abstraction of these mechanisms can use fitness measures to determine survival and reproduction a posteriori, since the struggle for existence is completely hidden in the evaluation process of individuals. The fact that different survival and reproduction constitute selection is valid in both cases, but in Evolutionary Algorithms fitness is measurable and implies the survival and reproduction behavior, which is just opposite to biological reality. This is simply an implication of the fitness-centered intention which necessarily prevails design and application of these algorithms. Therefore, it is just a logic consequence to model selection as an active, fitness-based component of Evolutionary Algorithms. However, how to model selection is by no means a simple problem. In evolutionary biology, it is usually distinguished between stabilizing, directed, and disruptive selection (see [Fut90], pp. 174–175). In the case of stabilizing selection, intermediate phenotypes have best fitness values, while disruptive selection is characterized by two or more distinct phenotypes that are highly fit and by intermediate phenotypes of low fitness (this assumes an - albeit unknown - ordering of phenotypes).

Keywords:   artificial life, bias, crossover, diversity, elitist, fitness, growth ratio, monotony, permutation, random walk

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