Evolutionary Algorithms for Solving Multi-Objective Problems
By: by Carlos A. Coello Coello, David A. Veldhuizen, Gary B. Lamont
Contributors: Veldhuizen, David A.; Lamont, Gary B.
Series: Genetic Algorithms and Evolutionary Computation -- 5; Genetic algorithms and evolutionary computation -- 5.
Publisher: Springer US
Published: 2002
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] /
Number of pages: 576
ISBN-13: 9781475751864; 9781475751840
Subjects: Artificial intelligence; Engineering; Information theory; Computer science; Operations research
Description: The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter. For additional information and supplementary teaching materials, please visit the authors' website at http://www.cs.cinvestav.mx/~EVOCINV/bookinfo.html.
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