| Summary: | An efficient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex optimization problems. This edited book presents a collection of recent advances in nature-inspired techniques for constrained numerical optimization. The book covers six main topics: swarm-intelligence-based approaches, studies in differential evolution, evolutionary multiobjective constrained optimization, hybrid approaches, real-world applications and the recent use of the artificial immune system in constrained optimization. Within the chapters, the reader will find different studies about specialized subjects, such as: special mechanisms to focus the search on the boundaries of the feasible region, the relevance of infeasible solutions in the search process, parameter control in constrained optimization, the combination of mathematical programming techniques and evolutionary algorithms in constrained search spaces and the adaptation of novel nature-inspired algorithms for numerical optimization with constraints. "Constraint-Handling in Evolutionary Optimization" is an important reference for researchers, practitioners and students in disciplines such as optimization, natural computing, operations research, engineering and computer science
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