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...

We present a general planning strategy to plan the motions of an agent having to explore a continuous state space in order to reach one or several goals. We propose a practical method to implement this technique based on a genetic algorithm and we illustrate the approach on the problem of controlling a mobile robot moving in a maze and looking for several items. Finally, we show that this planning strategy may serve as a possible control structure for an autonomous system. Problem Solving and planning 1 Introduction This study was motivated by our previous work on robot motion planning using a parallel genetic algorithm [8]. The planner we have design and implemented on a parallel machine is capable of planning collision free paths for a mobile robot placed among obstacles. The main advantage of this planner is its speed, it can plan complex paths such as the two paths represented in figure 1 in less than 0.5 second on a parallel machine with 64 Transputers. As a consequence it can b...

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.

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.

This report details the implementation of genetic algorithms as multiobjective optimisers using a specific example: the planning of a route composed of a number of straight pipeline segments from one point to another, whilst minimising first length and then environmental destruction caused by the construction of the pipeline. Using two genders, the algorithm is extended to take the two functions into account, both economic and environmental, in one reproductive plan. The use of sexual attractors between the two genders is detailed. Finally, possible extensions to the project are examined. 1 Introduction 1.1 Project Definition The aim of this project was to produce a program that would take a large array of "soil values" - biodiversity values for specific locations - and start and end points for a pipeline, and construct the pipeline which would consist of straight segments between the two points. The program should minimise both the length of the pipeline (and hence its cost) and a...

Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively

Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions. 1 INTRODUCTION Genetic Algorithms (GA) are a search paradigm that applies ideas from evolutionary biology ...

Transposition is a new genetic operator alternative to crossover and allows a classical GA to achieve better results. This mechanism characterized by the presence of mobile genetic units must be used with the right parameters to enable maximum performance to the GA. This paper presents the results of an empirical study which offers the main guidelines to choose the proper setting of parameters to use with transposition, which will lead the GA to the best solutions. 1 Introduction A Genetic Algorithm (GA) is an iterative search process that allows the search for solutions to a given problem in an intractable space. They are inspired in the biological processes of genetics and evolution, based on the Darwinian principle of the survival of the fittest. A randomly created initial population of candidates solutions to a given problem evolves through several generations. The evolution process is guaranteed by the genetic operators of selection and reproduction. In the classical GA, reprodu...

* * Corresponding author. Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.