Constraint-Handling in Evolutionary Optimization : Springer : 9783642101557 : 3642101550 : 28 Oct 2010 : Evolutionary algorithms (EAs), as well as other bio-inspired 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, real-world problems usually have constraints in their models. Therefore, a considerable amount of research has been d- icated to design and implement constraint-handling 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 constraint-handling technique is a key element in the design of competitiv