Evolutionary Algorithms for Solving Multi-Objective Problems - Carlos Coello Coello,David A. van Veldhuizen,Gary B. Lamont
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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-objec ... Full description
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Description
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.
More Information
| Author | Carlos Coello Coello, David A. van Veldhuizen, Gary B. Lamont |
|---|---|
| Publisher | Springer US |
| Series | Genetic and Evolutionary Computation |
| Release year | 2007 |
| Cover type | Hardcover |
| EAN | 9780387332543 |