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Practical Handbook of Genetic Algorithms

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advanced evolutionary computation techniques
artificial neural networks
Boltzmann Selection
Category=UYAM
Chunk Size
computational optimization
Cosmid Clone
Dynamic Scheduling Algorithms
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evolutionary algorithms
evolutionary computation
Execution Times
Fitness Function
Fragment Matches
GA
genetic algorithm coding
Genetic Sectoring
KDEL Sequence
Linear Ranking
Local Post-optimization
machine learning methods
MT
multi-niche crowding
Parallel GAs
PBIL
Proportional Selection
protein sequence analysis
robot motion planning
Scheduling Algorithms
Scheduling Overhead
Scheduling Steps
Search Algorithm
SSE
Structured Genetic Algorithm
Tabu Search Heuristic
VRP.

Product details

  • ISBN 9780849325298
  • Weight: 826g
  • Dimensions: 156 x 234mm
  • Publication Date: 15 Aug 1995
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them.
About
Chambers, Lance D.