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ConstraintHandling in Evolutionary Optimization (Studies in Computational Intelligence, 198),Used
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Evolutionary algorithms (EAs), as well as other bioinspired heuristics, are widely usedto solvenumericaloptimizationproblems.However,intheir or inal versions, they are limited to unconstrained search spaces i.e they do not include a mechanism to incorporate feasibility information into the ?tness function. On the other hand, realworld problems usually have constraints in their models. Therefore, a considerable amount of research has been d icated to design and implement constrainthandling techniques. The use of (exterior) penalty functions is one of the most popular methods to deal with constrained search spaces when using EAs. However, other alternative me ods have been proposed such as: special encodings and operators, decoders, the use of multiobjective concepts, among others. An e?cient and adequate constrainthandling technique is a key element in the design of competitive evolutionary algorithms to solve complex op mization problems. In this way, this subject deserves special research e?orts. After asuccessfulspecialsessiononconstrainthandlingtechniquesusedin evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, and motivated by the kind invitation made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bioinspired approaches. The intended audience for this book comprises graduate students, prac tionersandresearchersinterestedonalternativetechniquestosolvenumerical optimization problems in presence of constraints.
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