Carlos Coello Coello, Gary B. Lamont, David A. van Veldhuizen

Evolutionary Algorithms for Solving Multi-Objective Problems

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Genetic and Evolutionary Computation
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 11321620, 978-0-387-33254-3
• 2nd ed.
• Seitenzahl: 824
• Erscheinungstermin: 18. September 2007
• Englisch
• Abmessung: 241mm x 160mm x 49mm
• Gewicht: 1453g
• ISBN-13: 9780387332543
• ISBN-10: 0387332545
• Artikelnr.: 20923410

Autorenporträt

Carlos A. Coello Coello, CINVESTAV-IPN, México City, México / Gary B. Lamont, Air Force Institute of Technology, WPAFB, Dayton, OH, USA / David A. Van Veldhuizen, AFMC/DR, Wright-Patterson AFB, OH, USA

Inhaltsangabe

Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.