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.

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the selfadaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs.

After an outline of the history of evolutionary algorithms, a new (¯; ; ; ae) variant of the evolution strategies is introduced formally. Though not comprising all degrees of freedom, it is richer in the number of features than the meanwhile old (¯; ) and (¯+) versions. Finally, all important theoretically proven facts about evolution strategies are briefly summarized and some of many open questions concerning evolutionary algorithms in general are pointed out.

An extension of the evolution strategy for mixed-integer optimization problems is introduced. The resulting generalized evolution strategy is applied to the problem of optical multilayer coating design and the results are compared with results obtained by standard methods. The generalized evolution strategy as a synthesis method does not require the existence of a starting design, and it competes well with refinement methods for the optimization of starting designs. The results are very encouraging and indicate that this method is a robust and helpful algorithm for optical multilayer design. Furthermore, the generalized evolution strategy is not a tailored heuristic but can be used for arbitrary mixed-integer optimization problems. 1

The investigation of the performance of the Particle Swarm Optimization (PSO) method in Integer Programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used Branch and Bound technique, on several Integer Programming test problems. Results indicate that PSO handles efficiently such problems, and in most cases it outperforms the Branch and Bound technique.

Introduction The Gaussian distribution is the predominant choice for modeling noise frequently observable in measurings of various kinds. Here, we hold the view that a noise distribution with unbounded support (like the Gaussian, Cauchy, Laplace, Logistic, and others) may be quite unrealistic. Actually it is at least equally plausible to assume that the noise cannot exceed certain limits due to technical characteristics of the involved measurement unit. Even if a distributional shape close to a Gaussian appears reasonable we can resort to a symmetrical Beta distribution which can converge weakly to a Gaussian distribution under continuously increasing but bounded support (see e.g. Evans et al. 1993, p. 36). This assumption will have significant theoretical and practical impacts on the evolutionary algorithms (EAs) considered here. Traditional measures for coping with noisy fitness functions in evolutionary algorithms include the resampling of the random fitness value with averagi

The optimization of chemical engineering plants is still a challenging task. Economical evaluations of a process flowsheet using rigorous simulation models are very time consuming. Furthermore, many different types of parameters can be involved into the optimization procedure, resulting in highly restricted mixed-integer nonlinear objective functions. Evolution Strategies (ES) are a promising robust and flexible optimization technique for such problems. Motivated by a typical chemical process optimization problem, in this paper a non standard ES is presented, which deals with nominal discrete, metric integer and metric continuous parameters taken from limited domains. Genetic operators from literature are combined and adapted. Experimental results on test functions and an application example -- the parameter optimization of a HDA process -- show the robust convergence behaviour of the algorithm even for small population sizes. 1 Introduction The availability of high spe...

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The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a suitable representation of the variable space and appropriate genetic operators. In practice mainly problemspecific representations with specific genetic operators and miscellaneous extensions can be observed. In this connection it attracts attention that hardly any formal requirements on the genetic operators are stated. In this article we first formalize the representation problem and then propose a package of requirements to guide the design of genetic operators. By the definition of distance measures on the geno- and phenotype space it is possible to integrate problem-specific knowledge into the genetic operators. As an example we show how this package of requirements can be used to design a genetic programming (GP) system for finding Boolean functions. 1 Introduction The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a su...

Abstract. Evolution of quantum circuits faces two major challenges: complex and huge search spaces and the high costs of simulating quantum circuits on conventional computers. In this paper we analyze different selection strategies, which are applied to the Deutsch-Jozsa problem and the 1-SAT problem using our GP system. Furthermore, we show the effects of adding randomness to the selection mechanism of a (1,10) selection strategy. It can be demonstrated that this boosts the evolution of quantum algorithms on particular problems.