This paper is concerned with the application of genetic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be considered as a perturbation of some of the parameters. This suggests a genetic algorithm that uses real parameter vectors as chromosomes, real parameters as genes, and real numbers as alleles. Such an algorithm is proposed with two possible crossover methods. Schemata are defined for this algorithm, and it is shown that Holland's Schema theorem holds for one of these crossover methods. Experimental results are given that indicate that this algorithm with a mixture of the two crossover methods outperformed the binary-coded genetic algorithm on 7 of 9 test problems. Keywords: optimization, genetic algorithm, evolution To appear in: Foundations of Gen...

. 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 presents a theory of convergence for real-coded genetic algorithms---GAs that use floating-point or other high-cardinality codings in their chromosomes. The theory is consistent with the theory of schemata and postulates that selection dominates early GA performance and restricts subsequent search to intervals with above-average function value, dimension by dimension. These intervals may be further subdivided on the basis of their attraction under genetic hillclimbing. Each of these subintervals is called a virtual character, and the collection of characters along a given dimension is called a virtual alphabet. It is the virtual alphabet that is searched during the recombinative phase of the genetic algorithm, and in many problems this is sufficient to ensure that good solutions are found. Although the theory helps suggest why many problems have been solved using real-coded GAs, it also suggests that real-coded GAs can be blocked from further progress in those situations whe...

A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators---reproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch ...

Contents 1 Evolutionary Computation (introduction) 1.1 Methodology 1.1.1 Genetic Algorithms 1.1.2 Evolution Strategy 1.2 Applications 1.2.1 Protein Folding Simulation by Force Field Optimisation 1.2.1.1 Representation Formalism 1.2.1.2 Fitness Function 1.2.1.3 Conformational Energy 1.2.1.4 Genetic Operators 1.2.1.5 Ab initio Prediction Results 1.2.1.6 Side Chain Placement 1.2.2 Multi-Criteria Optimisation of Protein Conformations 1.2.2.1 Vector Fitness Function 1.2.2.2 Specialised Genetic Operators 1.2.2.3 Results Exercises References Evolutionary Computation Evolutionary Computation is, like neural networks, an example par excellence for an information processing paradigm that was originally developed and exhibited by nature and later discovered by man who subsequently transformed the general principle into computational algorithms to be put to work on computers. Nature makes in an impressive way use of the principle of genetic

This paper describes the development of an object-oriented parallel programming environment for genetic algorithms. This work, carried out as part of the ESPRIT III initiative PAPAGENA, intends to promote, develop and demonstrate the effectiveness of genetic algorithm (GA) and parallel genetic algorithm (PGA) techniques in a variety of real-world application domains. Central to this task is the development of a generalpurpose programming environment for both parallel and sequential genetic algorithms. GAME (Genetic Algorithm Manipulation Environment) will offer extensive tools for the design, configuration and monitoring of GA applications. This paper gives an overview of the design philosophy behind GAME, indicating the types of service and facilities the finished product will offer. Intrinsic to the design is the provision of an extensive multilevelled GA-specific library, offering GA and PGA applications, algorithms and operators. This will allow application developers the facilities to rapidly customise, configure and test novel GA and PGA designs. To sketch the types of application to be housed in GAME, a description of the applications currently under development within this project is also included. These range from finance through economic modelling to protein structure prediction. Key design requirements for GAME are versatility, together with flexibility. For this reason GAME has been designed to run within both Sun OS and PC DOS operating system, with or without parallel support. 1 Introduction