Evolutionary Algorithms for Solving Multi-Objective Problems

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

Overview

Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and shastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic shastic 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.

Product Details

ISBN-13:	9780387332543
Publisher:	Springer US
Publication date:	09/18/2007
Series:	Genetic and Evolutionary Computation
Edition description:	Second Edition 2007
Pages:	800
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

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.