Evolutionary Algorithms for Solving Multi Objective Problems (Genetic and Evolutionary Computation)

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Name:Evolutionary Algorithms for Solving Multi Objective Problems (Genetic and Evolutionary Computation)

Total Size: 10.10 MB

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Springer.Evolutionary.Algorithms.for.Solving.Multi-Objective.Problems.2nd.Edition.Sep.2007.pdf (Size: 10.10 MB) (Files: 1)


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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.

ISBN: 0387332545
Publisher: Springer
Date: 18 September, 2007
Author: Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen

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