Constraint-Handling in Evolutionary Optimization - Studies in Computational Intelligence 2009 edition

Marc Notes: Includes bibliographical references and index. Jacket Description/Back: 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. Table of Contents: Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm.- Boundary Search for Constrained Numerical Optimization Problems.- Solving Difficult Constrained Optimization Problems by the ? Constrained Differential Evolution with Gradient-Based Mutation.- Constrained Real-Parameter Optimization with ? -Self-Adaptive Differential Evolution.- Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization.- An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization.- Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.- On GA-AIS Hybrids for Constrained Optimization Problems in Engineering.- Constrained Optimization Based on Quadratic Approximations in Genetic Algorithms.- Constraint-Handling in Evolutionary Aerodynamic Design.- Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey. Publisher Marketing: 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 competitive evolutionary algorithms to solve complex op- mization problems. In this way, this subject deserves special research e?orts. After asuccessfulspecialsessiononconstraint-handlingtechniquesusedin 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 bio-inspired approaches. The intended audience for this book comprises graduate students, prac- tionersandresearchersinterestedonalternativetechniquestosolvenumerical optimization problems in presence of constraints