Overview
- Designed for courses on Evolutionary Multi-objective Optimization and Evolutionary Algorithms
- 2nd Edition is completely updated and presents the latest research
- Provides a complete set of teaching tutorials, exercises and solutions
- Contains exhaustive appendices, index and bibliography
- Includes supplementary material: sn.pub/extras
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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About this book
Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems.
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations.
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Table of contents (10 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Evolutionary Algorithms for Solving Multi-Objective Problems
Authors: Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-0-387-36797-2
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2007
Hardcover ISBN: 978-0-387-33254-3Published: 18 September 2007
Softcover ISBN: 978-1-4899-9460-8Published: 28 October 2014
eBook ISBN: 978-0-387-36797-2Published: 26 August 2007
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 2
Number of Pages: XXI, 800
Topics: Programming Techniques, Theory of Computation, Optimization, Probability Theory and Stochastic Processes, Algorithm Analysis and Problem Complexity, Artificial Intelligence