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Evolutionary Algorithms for Solving Multi-Objective Problems - Carlos Coello Coello - Bog

Bag om 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.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780387332543
  • Indbinding:
  • Hardback
  • Sideantal:
  • 824
  • Udgivet:
  • 18. september 2007
  • Udgave:
  • 07002
  • Størrelse:
  • 160x49x241 mm.
  • Vægt:
  • 1385 g.
  • 8-11 hverdage.
  • 7. december 2024
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Beskrivelse af 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.

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