Evolutionary Computation: An Overview

| In this paper, we present an overview of the most important representatives of algorithms gleaned from natural evolution, so-called evolutionary algorithms. Evolution strategies, evolutionary programming, and genetic algorithms are summarized, with special emphasis on the principle of strategy parameter self-adaptation utilized by the rst two algorithms to learn their own strategy parameters such as mutation variances and covariances. Some experimental results are presented which demonstrate the working principle and robustness of the self-adaptation methods used in evolution strategies and evolutionary programming. General principles of evolutionary algorithms are discussed , and we identify certain properties of natural evolution which might help to improve the problem solving capabilities of evolutionary algorithms even further. More than 30 years ago, a number of innovative researchers at diierent places in the US and Europe independently came up with the idea of mimicking mechanisms of biological evolution in order to develop powerful algorithms for problems of adaptation and optimization. Since many optimal structures like the shape of birds' wings or the branching structure of blood vessels have emerged through biological evolution, the idea to utilize the underlying mechanism for the solution of optimization problems has motivated a considerable amount of research, resulting in several approaches that have proven their effectiveness and robustness in a variety of applications (see e.g. the bibliography on evolutionary algorithms collected by Alander 1]). Typically, an optimization application requires nding a setting ~ x = (x 1 ; : : :; x n) 2 M of free parameters of the system under consideration, such that a certain quality criterion f : M ! IR (typically called the objective function) is maximized (or, equivalently, minimized): f(~ x) ! max : (1) The objective function might be given by real-world systems of arbitrary complexity (e.g., a reactor for biochemical processes 21] or a two-phase nozzle 32]), by simulation models implemented on a computer (e.g., a nuclear reactor simulation 6]), or it might be given by an analytical expression. Typically, a solution to the global optimization problem (1) requires nding a vector ~ x such that 8~ x 2 M : f(~ x) f(~ x) = f. Characteristics such as multimodality, i.e., the existence of local maxima ~ x 0 with 9" > 0 : 8~ x 2 M : k~ x ? ~ x 0 k < ") f(~ x) f(~ x 0) (2) such that the set of feasible solutions is only …