Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP), by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies---fuzzy systems and evolutionary algorithms---so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable. 1999 Elsevier Science B.V. All rights reserved. Keywords: Fuzzy systems; Genetic algorithms; Breast cancer diagnosis www.elsevier.com/locate/artmed 1.

This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is elaborated. Here, it is shown that substantial improvement of image quality is obtained by using the genetic approach.

In this paper, evolution strategies (ESs)--- a class of evolutionary algorithms using normally distributed mutations, recombination, deterministic selection of the � � ? 1 best offspring individuals, and the principle of self-adaptation for the collective on-line learning of strategy parameters--- are described by demonstrating their differences to genetic algorithms. By comparison of the algorithms, it is argued that the application of canonical genetic algorithms for continuous parameter optimization problems implies some difficulties caused by the encoding of continuous object variables by binary strings and the constant mutation rate used in genetic algorithms. Because they utilize a problem-adequate representation and a suitable self-adaptive step size control guaranteeing linear convergence for strictly convex problems, evolution strategies are argued to be more adequate for continuous problems. The main advantage of evolution strategies, the self-adaptation of strategy parameters, is explained in detail, and further components such as recombination and selection are described on a rather general level. Concerning theory, recent results regarding convergence velocity and global convergence of evolution strategies are briefly summarized, especially including the results for (��,)-ESs with recombination. It turns out that the theoretical ground of ESs provides many more results about their behavior as optimization algorithms than available for genetic algorithms, and that ESs have all properties required for global optimization methods. The paper concludes by emphasizing the necessity for an appropriate step size control and the recommendation to avoid encoding mappings by using a problem-adequate representation of solutions within evolutionary algorithms.

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 first 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. I. Evolutionary Computation and Optimization More than 30 years ago, a number of innovative researchers at different places in the US and Europe independe...

Evolutionary algorithms are an important emergent computing methodology. They have aroused intense interest in the past few years because of their versatility in solving difficult problems in the optimization and machine learning fields. Many applications to several different areas have been reported and the field is still in expansion. We will first briefly review the history and the methodological basis of evolutionary algorithms, followed by a simple example of their functioning. Parallel evolutionary algorithms will then be introduced, showing their good match to today's parallel and distributed computers. We will then look at a couple of applications and, finally, references and comments to bibliographic and other information on evolutionary methods will be given to allow readers to broaden their knowledge in the field. 1 Introduction Evolutionary Algorithms (EAs) are a hot topic these days. Although they are probably a fashionable theme, there is also much solid work being done ...

Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem---breast cancer diagnosis---obtaining the best results to date while expending less computational effort than formerly. Analyzing our results, we derive guidelines for setting the algorithm's parameters given a (hard) problem to solve. We hope Fuzzy CoCo proves to be a powerful tool in the fuzzy modeler's toolkit.

A typical vehicle routing problem can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points (cities, stores, warehouses, schools, customers etc). The routes must be designed in such a way that each point is visited only once by exactly one vehicle, all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. The vehicle routing problem with time windows is a generalization of the standard vehicle routing problem involving the added complexity that every customer should be served within a given time window. In this paper we review shortly the developed genetic algorithm based approaches for solving the vehicle routing problem with time windows and compare their performance with the best recent metaheuristic algorithms. The findings indicate that the results obtained with pure genetic algorithms are not competitive with the best published results, though the differences are not overwhelming. 1

Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem---breast cancer diagnosis--- obtaining the best results to date while expending less computational effort than formerly. 1 Introduction In recent years the natural phenomenon of coevolution---the simultaneous, coupled evolution of two or more species--- has been explored by evolutionary-computation practitioners, who introduced the notion of coevolutionary algorithms. It has been shown that, for certain problem domains, coevolution produces better solutions while incurring a lower computational cost. We explore herein the application of coevolution to the design of fuzzy systems, int...

Evolutionary computation techniques have received a lot of attention regarding their potential as optimization techniques for complex real-world problems. These techniques, based on the powerful principle of "survival of the fittest", model some natural phenomena of genetic inheritance and Darwinian strife for survival; they also constitute an interesting category of modern heuristic search. This introductory article presents the main paradigms of evolutionary algorithms (genetic algorithms, evolution strategies, evolutionary programming, genetic programming) as well as other (hybrid) methods of evolutionary computation. Two particular research directions (parallel evolutionary techniques and self-adaptation) are discussed further in the last part of this paper. 1 Introduction The evolutionary computation (EC) techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival. As stated in [33]: "... the metaph...