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.

Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is exa...

. 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 proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called life-time fitness evaluation (LTFE). Two unrelated problems - neural net learning and constraint satisfaction - are used to illustrate the approach. Both problems use predator-prey interactions to boost the search. In contrast with traditional "single population " genetic algorithms (GAs), two populations constantly interact and coevolve. However, the same algorithm can also be used with different types of coevolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation. The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with co...

Genetic algorithms rely on two genetic operators - crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.

A rigorous formulation of the generalisation of schema analysis known as forma analysis is presented. This is shown to provide a direct mechanism for harnessing knowledge about a search space, codified through the imposition of equivalence relations over that space, to generate a genetic representation and operators. It is shown that a single characterisation of a space leads to a unique genetic representation, and the kinds of representations that are possible are classified and discussed. A relatively new operator, call random assorting recombination (RARw ), is defined rigorously and is shown to be, in an important sense, a universal recombination operator.

. The role of the mutation rate in canonical genetic algorithms is investigated by comparing a constant setting, a deterministically varying, time-dependent mutation rate schedule, and a self-adaptation mechanism for individual mutation rates following the principle of self-adaptation as used in evolution strategies. The power of the self-adaptation mechanism is illustrated by a time-varying optimization problem, where mutation rates have to adapt continuously in order to follow the optimum. The strengths of the proposed deterministic schedule and the selfadaptation method are demonstrated by a comparison of their performance on difficult combinatorial optimization problems (multiple knapsack, maximum cut and maximum independent set in graphs). Both methods are shown to perform significantly better than the canonical genetic algorithm, and the deterministic schedule yields the best results of all control mechanisms compared. 1 Introduction Genetic Algorithms [11, 14] are the best kno...

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This paper considers the problem of using genetic algorithms to design structures. We relax one constraint on classical genetic algorithms and describe a genetic algorithm that uses differential information about search direction to design structures. This differential information is captured by a masked crossover operator which also removes the bias toward short schemas. We analyze performance and present some preliminary results. Further, consideration of this problem suggests a partial solution to the identification of the deception problem. 1 INTRODUCTION The problem of designing structures is pervasive in science and engineering. The problem is: Given a function and some materials to work with, design a structure that performs this function subject to certain constraints. As an example of the design problem, consider the combinational circuit design problem: Given a set of logic gates, design a circuit that performs a desired function. Two instantiations of this problem are the...

The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments. Keywords: genetic algorithms, non-coding segments, non-coding DNA, introns, Royal Road function. 1 Introduction Na...