. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...

This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies. Besides describing briefly each of these approaches (or groups of techniques), we provide some criticism regarding their highlights and drawbacks. A small comparative study is also conducted, in order to assess the performance of several penalty-based approaches with respect to a dominance-based technique proposed by the author, and with respect to some mathematical programming approaches. Finally, we provide some guidelines regarding how to select the most appropriate constraint-handling technique for a certain application, ad we conclude with some of the the most promising paths of future research in this area.

s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

In this paper we describe a method for hybridizing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their weighted distance from the members of the training set. We applied our hybrid algorithm to three test cases. Classification results obtained with the hybrid algorithm exceed the performance of the k nearest neighbors algorithm in all three cases. 1

Despite the extended applicability of evolutionary algorithms to a wide range of domains, the fact that these algorithms are unconstrained optimization techniques leaves open the issue regarding how to incorporate constraints of any kind (linear, non-linear, equality and inequality) into the fitness function as to search efficiently. The main goal of this paper is to provide a detailed and comprehensive survey of the many constraint handling approaches that have been proposed for evolutionary algorithms, analyzing in each case their advantages and disadvantages, and concluding with some of the most promising paths of research.

this paper. When a GP run has finished, the final output of the GP subsystem is a single controller program. This controller program is the one which, when used to govern the actions of a simulated agent, resulted in the best rated agent according to the user-supplied fitness metric. This controller program may then be used to generate the motion control needed for the animation.

. The processes of adaptation in natural organisms consist of two complementary phases: 1) learning, occurring within each individual 's lifetime, and 2) evolution, occurring over successive generations of the population. In this paper, we study the relationship between learning and evolution in a simple abstract model, where neural networks capable of learning are evolved using genetic algorithms (GAs). Individuals try to maximize their life energy by learning certain rules that distinguish between two groups of materials: food and poison. The connective weights of individuals' neural networks undergo modication, i.e., certain characters will be acquired, through their lifetime learning. By setting various rates for the heritability of acquired characters, which is a motive force of Lamarckian evolution, we observe adaptational processes of populations over successive generations. Paying particular attention to behaviours under changing environments, we show the following results. Po...

Miniature rovers with articulated mobility mechanisms are being developed for planetary surface exploration on Mars and small solar system bodies. These vehicles are designed to be capable of autonomous recovery from overturning during surface operations. This paper describes a proposed computational means of developing motion behaviors that achieve the autonomous recovery function. Its aim is to reduce the effort involved in developing self-righting control behaviors. The approach is based on the integration of evolutionary computing with a dynamics simulation environment for evolving and evaluating motion behaviors. The automated behavior design approach is outlined and its underlying genetic programming infrastructure is described. 1 INTRODUCTION Recent advances in micro-technology and mobile robotics have enabled the development of scientifically capable rovers of mass on the order of tens or hundreds of grams. Development of such nanorovers will permit mobility-based science sur...

. Genetic algorithms are now among the most promising optimization techniques. They are based on the following reasonable idea. Suppose that we want to maximize an objective function J(x). We somehow choose the first generation of "individuals" x 1 ; x 2 ; :::; x n (i.e., possible values of x) and compute the "fitness" J(x i ) of all these individuals. To each individual x i , we assign a survival probability p i that is proportional to its fitness. In order to get the next generation we then repeat the following procedure k times: take two individuals at random (i.e., x i with probability p i ) and "combine" them according to some rule. For each individual of this new generation, we also compute its fitness (and survival probability), "combine" them to get the third generation, etc. Under certain reasonable conditions, the value of the objective function increases from generation to generation and converges to a maximal value. The performance of genetic algorithms can be essentially improved if we use fitness scaling, i.e., use f(J(x i )) instead of J(x i ) as a fitness value, where f(x) is some fixed function that is called a scaling function. The efficiency of fitness scaling essentially depends on the choice of f . So what f should we choose? In the present paper we formulate the problem of choosing f as a mathematical optimization problem and solve it under different optimality criteria. As a result, we get a list of functions f that are optimal under these criteria. This list includes both the functions that were empirically proved to be the best for some problems, and some new functions that may be worth trying. 1 1.

Dealing with synthetic actors who move and behave realistically in virtual environments is a task which involves different disciplines like Mechanics, Physics, Robotics, Artificial Intelligence, Artificial Life, Biology, Cognitive Sciences and so on. In this paper we use the nature of the information required for controlling actors' motion and behaviour to propose a new classi"cation of synthetic actors. A description of the different motion and behaviour techniques is presented. A set of Internet addresses of the most relevant research groups, commercial companies and other related sites in this