menu
{ "item_title" : "Genetic Algorithm Essentials", "item_author" : [" Oliver Kramer "], "item_description" : "This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations.The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/31/984/834/3319848348_b.jpg", "price_data" : { "retail_price" : "159.99", "online_price" : "159.99", "our_price" : "159.99", "club_price" : "159.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Genetic Algorithm Essentials|Oliver Kramer

Genetic Algorithm Essentials

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations.
The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

This item is Non-Returnable

Details

  • ISBN-13: 9783319848341
  • ISBN-10: 3319848348
  • Publisher: Springer
  • Publish Date: July 2018
  • Dimensions: 9.21 x 6.14 x 0.22 inches
  • Shipping Weight: 0.35 pounds
  • Page Count: 92

Related Categories

You May Also Like...

    1

BAM Customer Reviews