Abstract � Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation � it tunes the algorithm to the problem while solving the problem. In this paper we develop a classi�cation of adaptation on the basis of the mechanisms used � and the level at which adaptation operates within the evolutionary algorithm. The classi�cation covers all forms of adaptation in evolutionary computation and suggests fur� ther research. I.

. The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the final results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. In this paper, we study these techniques in depth, based on the use of fuzzy logic controllers. Furthermore, we design and discuss an adaptive realcoded genetic algorithm based on the use of fuzzy logic controllers. Although suitable results have been obtained by using this type of adaptive technique, we report some reflections on open problems that still remain. Keywords. Exploitation/exploration relationship, adaptive genetic algorithms, fuzzy logic controllers. 1 Introduction GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities t...

There are several issues that need to be taken in consideration when designing a hybrid problem solver. This paper focuses on one of them---decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hillclimber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently. I. Introduction It is very unlikely that a GA will outperform a specialized scheme tailored to a problem. However, a combination of the two usually performs better than either one alone. This happens because on a hybrid there is the possibility of incorporating do...

. Exploration and exploitation are the two cornerstones of problem solving by search. The common opinion about evolutionary algorithms is that they explore the search space by the (genetic) search operators, while exploitation is done by selection. This opinion is, however, questionable. In this paper we give a survey of different operators, review existing viewpoints on exploration and exploitation, and point out some discrepancies between and problems with current views. 1. Introduction Evolutionary algorithms (EA) belong to the family of stochastic generate-and-test search algorithms [28]. There are different types of EAs, the most common classification distinguishes Genetic Algorithms (GA), Evolution Strategies (ES) and Evolutionary Programming (EP), [4]. A fourth type of EA, Genetic Programming (GP) has grown out of GAs and is often seen as a sub-class of them. Besides the different historical roots and philosophy there are also technical differences between the three mai...

Self-adaption is one of the most promising areas of research in evolutionary computation as it adapts the algorithm to the problem while solving the problem. In this paper we extend self-adaption to operate on more than one aspect of evolutionary computation and at more than one level of adaption. We developed a genetic algorithm which self-adapts both mutation strength and population size; the results indicate that the approach works quite well. 1 Introduction Since evolutionary algorithms implement the idea of evolution, it is more than natural to expect some self-adapting characteristics of these techniques. Apart from evolutionary strategies, which incorporate some of its control parameters in the solution vectors, most other techniques use fixed representations, operators, and control parameters. Some of the promising research areas based on the inclusion of self adapting mechanisms are: ffl representation of individuals (as proposed by Shaefer (1987); the Dynamic Parameter Enco...

Genetic algorithms (GAs) have been used to solve difficult optimization problems in a number of fields. One of the advantages of these algorithms is that they operate well even in domains where little is known, thus giving the GA the flavor of a general purpose problem solver. However, in order to solve a problem with the GA, the user usually has to specify a number of parameters that have little to do with the user's problem, and have more to do with the way the GA operates. This dissertation presents a technique that greatly simplifies the GA operation by relieving the user from having to set these parameters. Instead, the parameters are set automatically by the algorithm itself. The validity of the approach is illustrated with artificial problems often used to test GA techniques, and also with a simplified version of a network expansion problem.

In this study, we give a brief overview of search strategy adaptation in evolutionary computation. The

An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offspring; and an adaptation rule, that modifies the selection mechanism based on the rewards of the different operators. This paper is concerned with the latter, and proposes a new approach for it based on the well-known Multi-Armed Bandit paradigm. However, because the basic Multi-Armed Bandit methods have been developed for static frameworks, a specific Dynamic Multi-Armed Bandit algorithm is proposed, that hybridizes an optimal Multi-Armed Bandit algorithm with the statistical Page-Hinkley test, which enforces the efficient detection of changes in time series. This original Operator Selection procedure is then compared to the state-of-the-art rules known as Probability Matching and Adaptive Pursuit on several artificial scenarios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outperform the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best.

. In the vast majority of genetic algorithm implementations, the operator probabilities are fixed throughout a given run. However, it may be useful to adjust these probabilities during the run, according to the ability of the operators to produce children of increased fitness. Cost Based Operator Rate Adaptation (COBRA) periodically re-ranks operator probabilities according to a measure of operator performance. The effect upon genetic algorithm performance of COBRA upon both well-studied theoretical and practical problems is examined. 1 Introduction It has long been acknowledged that the choice of operator probabilities have a significant impact upon GA performance. However, finding a good choice is somewhat of a black art. The appropriate choice depends upon the other GA components, such as the population model, the problem to be solved, its representation, and the operators used. The above also ignores the case for varying operator probabilities during the course of a GA run. Davis ...

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...