Constraint-Handling in Evolutionary Optimization

  • Efrén Mezura-Montes

Part of the Studies in Computational Intelligence book series (SCI, volume 198)

Table of contents

  1. Front Matter
  2. Angel E. Muñoz Zavala, Arturo Hernández Aguirre, Enrique R. Villa Diharce
    Pages 1-23
  3. Guillermo Leguizamón, Carlos Coello Coello
    Pages 25-49
  4. Tapabrata Ray, Hemant Kumar Singh, Amitay Isaacs, Warren Smith
    Pages 145-165
  5. Heder S. Bernardino, Helio J. C. Barbosa, Afonso C. C. Lemonge, Leonardo G. Fonseca
    Pages 167-192
  6. Marcella C. Araujo, Elizabeth F. Wanner, Frederico G. Guimarães, Ricardo H. C. Takahashi
    Pages 193-217
  7. Back Matter

About this book


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.


Computational Intelligence Constraint-Handling Evolutionary Optimization Mutation Operator algorithm algorithms artificial intelligence evolutionary algorithm evolutionary computation genetic algorithms heuristics intelligence multi-objective optimization optimization

Editors and affiliations

  • Efrén Mezura-Montes
    • 1
  1. 1.National Laboratory on Advanced Informatics (LANIA A.C.) Rébsamen 80, CENTROXalapaMexico

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-00618-0
  • Online ISBN 978-3-642-00619-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
Chemical Manufacturing
IT & Software
Energy, Utilities & Environment
Oil, Gas & Geosciences