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Industrial Applications of Genetic Algorithms (International Series on Computational Intelligence)
 
 
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Industrial Applications of Genetic Algorithms (International Series on Computational Intelligence) [Hardcover]

Charles Karr (Editor), L. Michael Freeman (Editor)

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Book Description

International Series on Computational Intelligence December 29, 1998
Genetic algorithms (GAs) are computer-based search techniques patterned after the genetic mechanisms of biological organisms that have adapted and flourished in changing, highly competitive environments for millions of years. GAs have been successfully applied to problems in a variety of studies, and their popularity continues to increase because of their effectiveness, applicability, and ease of use.
Industrial Applications of Genetic Algorithms shows how GAs have made the leap form their origins in the laboratory to the practicing engineer's toolbox. Each chapter in the book describes a project completed by a graduate student at the University of Alabama.

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Inside This Book (learn more)
First Sentence:
Genetic algorithms (GAs) are computer-based search techniques patterned after the genetic mechanisms of biological organisms which have allowed such organisms to adapt and flourish in changing, highly competitive environments for millions of years. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
function evaluation simulation, single child offspring, cyclometric complexity, hydrocyclone model, unit test data, multiplexor problem, fitness algorithm, porous liner, cost functional value, slurry input, corridor grids, data mining environment, quadrature nodes, random mutation operator, transpiration cooling, simulation converged, genetic algorithm parameters, unmatched items, split size, learning classifier system, system identification problem, adaptive unit, using genetic programming, control graph, navigation algorithms
Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Generation Figure, Fitness Best Fitness Solution, University of Michigan, Non-Coplanar Orbital Transfers, Addison-Wesley Publishing Company, Database Pop, Marshall Space Flight Center, Tuning Bama Optimized Neural Networks, Ann Arbor, Sante Fe Institute, Academic Press, Artificial Eco-System, Function Operator Operator, Generations Figure, Mobile Robot Wall-Following Algorithms, San Diego, San Mateo, Van Nostrand Reinhold, Variables Fitness Crossover Mutation Case, Bama Optimized Recurrent Neural Network, Cambridge University Press, Computer Moves, Dissertation Abstracts International, Human Moves
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