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Success in Evolutionary Computation [Hardcover]

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  • Category: Books (Mathematics)
  • ISBN-10:  354076285X
  • ISBN-10:  354076285X
  • ISBN-13:  9783540762850
  • ISBN-13:  9783540762850
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  372
  • Pages:  372
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Feb-2008
  • Pub Date:  01-Feb-2008
  • SKU:  354076285X-11-SPRI
  • SKU:  354076285X-11-SPRI
  • Item ID: 100893471
  • List Price: $219.99
  • Seller: ShopSpell
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  • Delivery by: Jan 01 to Jan 03
  • Notes: Brand New Book. Order Now.

Evolutionary Computation (EC) includes a number of techniques such as Genetic Algorithms which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting and rapidly-evolving field.

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.

Theory.- Adaptation of a Success Story in GAs: Estimation-of-Distribution Algorithms for Tree-based Optimization Problems.- The Automated Design of Artificial Neural Networks Using Evolutionary Computation.- A Versatile Surrogate-Assisted Memetic Algorithm for Optimization of Computationally Expensive Functions and its Engineering Applications.- Data Mining and Intelligent Multi-Agent Technologies in Medical Informatics.- Applications.- Evolving Trading Rules.- A Hybrid Genetic Algorithm for the Protein Folding Problem Using the 2D-HP Lattice Model.- Optimal Management of Agricultural Systems.- Evolutionary Electlc,
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