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Advances in Evolutionary Algorithms Theory, Design and Practice [Paperback]

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  • Category: Books (Mathematics)
  • Author:  Ahn, Chang Wook
  • Author:  Ahn, Chang Wook
  • ISBN-10:  364206860X
  • ISBN-10:  364206860X
  • ISBN-13:  9783642068607
  • ISBN-13:  9783642068607
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Feb-2010
  • Pub Date:  01-Feb-2010
  • SKU:  364206860X-11-SPRI
  • SKU:  364206860X-11-SPRI
  • Item ID: 100710926
  • List Price: $109.99
  • Seller: ShopSpell
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  • Delivery by: Jul 04 to Jul 06
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Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated:

  • Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines.
  • Demonstrating the practical use of the suggested road map.
  • Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications.
  • Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain.
  • Opening an important track for multiobjective GEA research that relies on decomposition principle.

This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.

Practical Genetic Algorithms.- Real-World Application: Routing Problem.- Elitist Compact Genetic Algorithms.- Real-coded Bayesian Optimization Algorithm.- Multiobjective Real-coded Bayesian Optimization Algorithm.- Conclusions.

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide l“\

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