Evolutionary Algorithms for Solving Multi-Objective Problems

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.

List 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.

Summary

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.

Product details

• Authors: Carlo Coello Coello, Carlos Coello Coello, Gary Lamont, Gary B Lamont, Gary B. Lamont, van Veldhuize, David A. van Veldhuizen, David A. van Veldhuizen
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2014
• EAN: 9781489994608
• ISBN: 978-1-4899-9460-8
• No. of pages: 800
• Dimensions: 154 mm x 234 mm x 41 mm
• Weight: 1240 g
• Illustrations: XXI, 800 p.
• Series: Genetic and Evolutionary Computation; Genetic and Evolutionary Computation
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; B, Algorithms, Optimization, Artificial Intelligence, computer science, Theory of Computation, Programming Techniques, Probability Theory and Stochastic Processes, Computer programming, Probability & statistics, Computers, Algorithms & data structures, Probabilities, Stochastics, Probability Theory, Mathematical theory of computation, Mathematical optimization, Algorithm Analysis and Problem Complexity