Evolutionary Algorithms for Solving Multi-Objective Problems

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

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English

Springer-Verlag New York Inc.
28 October 2014

Probability & statistics; Optimisation; Stochastics; Computer programming & software development; Mathematical theory of computation

Series: Genetic and Evolutionary Computation

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.

By: Carlos Coello Coello, Gary B. Lamont, David A. van Veldhuizen
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: 2nd ed. 2007
Dimensions: Height: 235mm, Width: 155mm, Spine: 41mm
Weight: 1.240kg
ISBN: 9781489994608
ISBN 10: 1489994602
Series: Genetic and Evolutionary Computation
Pages: 800
Publication Date: 28 October 2014
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.