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2018 | OriginalPaper | Buchkapitel

Robust Evolutionary Optimization Based on Coevolution

verfasst von : Steffen Limmer, Tobias Rodemann

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

A way to deal with uncertainties in the fitness function of an optimization problem is robust optimization, which optimizes the expected value of the fitness. In the context of evolutionary optimization, it is a common practice to compute the expected value of the fitness approximately with the help of Monte-Carlo simulation. This approach requires a lot of evaluations of the fitness function in order to evaluate an individual and thus it can be very compute-intensive.

In the present paper, we propose a coevolution-based approach for the robust optimization of problems with a fitness function basically depending on discrete random variables, which conditionally depend on the decision variables. Experiments on three benchmark functions show that the approach yields a good trade-off between the number of required fitness function evaluations and the quality of the results.

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Vorheriges Kapitel Search-Based Temporal Testing in an Embedded Multicore Platform

Nächstes Kapitel On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization

In the rest of the paper, we assume that the optimization problem at hand is a minimization problem.

Since \({\varvec{Y}}\) is assumed to be discrete, g might be undefined for \(\mathbb {E}({\varvec{Y}}|{\varvec{x}})\). In this case, the most probable value of \({\varvec{Y}}\) given \({\varvec{x}}\) can be used instead of the expected value.

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Titel: Robust Evolutionary Optimization Based on Coevolution
verfasst von: Steffen Limmer
Tobias Rodemann
Verlag: Springer International Publishing
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-319-77537-1

Electronic ISBN: 978-3-319-77538-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-77538-8_54

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