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OverviewSolving 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 DetailsAuthor: Carlos Coello Coello , Gary B. Lamont , David A. van VeldhuizenPublisher: 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: 9781489994608ISBN 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 ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsBasic 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |