Evolutionary Algorithms for Solving Multi-Objective Problems

Author:   Carlos Coello Coello ,  Gary B. Lamont ,  David A. van Veldhuizen
Publisher:   Springer-Verlag New York Inc.
Edition:   2nd ed. 2007
ISBN:  

9781489994608


Pages:   800
Publication Date:   28 October 2014
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Evolutionary Algorithms for Solving Multi-Objective Problems


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Overview

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.

Full Product Details

Author:   Carlos Coello Coello ,  Gary B. Lamont ,  David A. van Veldhuizen
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2nd ed. 2007
Dimensions:   Width: 15.50cm , Height: 4.10cm , Length: 23.50cm
Weight:   1.240kg
ISBN:  

9781489994608


ISBN 10:   1489994602
Pages:   800
Publication Date:   28 October 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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.

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