Parameter control in differential evolution for constrained optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A review of constraint-handling techniques for evolution strategies
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
On the behaviour of the (1,λ)-es for a simple constrained problem
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
An improved (µ+λ)-constrained differential evolution for constrained optimization
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
International Journal of Bio-Inspired Computation
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This book is the result of a successful special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The book covers six main topics: The first two chapters refer to swarm- intelligence-based approaches. Differential evolution, a very competitive evolutionary algorithm for constrained optimization, is studied in the next three chapters. Two different constraint-handling techniques for evolutionary multiobjective optimization are presented in the two subsequent chapters. Two hybrid approaches, one with a combination of two nature-inspired heuristics and the other with the mix of a genetic algorithm and a local search operator, are detailed in the next two chapters. Finally, a constraint-handling technique designed for a real-world problem and a survey on artificial immune system in constrained optimization are the subjects of the final two chapters. The intended audience for this book comprises graduate students, practitioners and researchers interested on alternative techniques to solve numerical optimization problems in presence of constraints.