The Practical Handbook of Genetic Algorithms: New Frontiers, Volume 2

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Lance D. Chambers
CRC Press, Aug 15, 1995 - Mathematics - 448 pages
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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.
 

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Contents

MultiNiche Crowding for MultiModal Search
5
Artificial Neural Network Evolution Learning to Steer a Land Vehicle
31
Locating Putative Protein Signal Sequences
53
Selection Methods for Evolutionary Algorithms
67
Parallel Cooperating Genetic Algorithms An Application to Robot Motion Planning
93
The Boltzmann Selection Procedure
111
Structure and Performance of FineGrain Parallelism in Genetic Search
139
Parameter Estimation for a Generalized Parallel Loop Scheduling Algorithm
155
A Hybrid Approach Using Neural Networks Simulation Genetic Algorithms and Machine Learning for RealTime Sequencing and Scheduling Problems
197
Chemical Engineering
221
Vehicle Routing with Time Windows using Genetic Algorithms
253
Evolutionary Algorithms and Dialogue
279
Incorporating Redundancy and Gene Activation Mechanisms in Genetic search for Adapting to NonStationary Environments
303
Input Space Segmentation with a Genetic Algorithm for Generation of Rule Based Classifier Systems
317
An Indexed Bibliography of Genetic Algorithms
333
INDEX
429

Controlling a Dynamic Physical Systems Using Genetic Based Learning Methods
173

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Page 24 - This work was supported in part by the UK Science and Engineering Research Council, under grant GR/D97757, and in part by the Applied Mathematical Sciences subprogram of the Office of Energy Research, US Department of Energy, under contract W-31-109-Eng-38. References [1] Khayri AM Ali. Or-parallel execution of Prolog on BC-Machine.
Page 25 - K. Deb and DE Goldberg, An investigation of niche and species formation in genetic function optimization...

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