Practical Handbook of Genetic Algorithms

New Frontiers, Volume II

Inbunden, Engelska, 1995

2 919 kr

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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.

Produktinformation

Utgivningsdatum1995-08-15
Mått156 x 234 x 33 mm
Vikt826 g
FormatInbunden
SpråkEngelska
Antal sidor448
FörlagTaylor & Francis Inc
ISBN9780849325298

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Systemvetenskap och AI inom Data och it

ContentsIntroductionMulti-Niche Crowding for Multi-modal SearchIntroduction Genetic Algorithms for Multi-modal Search Application of MNC to Multi-modal Test Functions Application to DNA Restriction Fragment Map Assembly Results and Discussion Conclusions Previous Related Work and Scope of Present Work Appendix Artificial Neural Network Evolution: Learning to Steer a Land VehicleOverview Introduction to Artificial Neural Networks Introduction to ALVINN The Evolutionary Approach Task Specifics Implementation and Results Conclusions Future Directions Locating Putative Protein Signal SequencesIntroduction Implementation Results of Sample Applications Parametrization Study Future Directions Selection Methods for Evolutionary AlgorithmsFitness Proportionate Selection (FPS) Windowing Sigma Scaling Linear Scaling Sampling Algorithms Ranking Linear Ranking Exponential Ranking Tournament Selection Genitor or Steady State Models Evolution Strategy and Evolutionary Programming Methods Evolution Strategy Approaches Top-n Selection Evolutionary Programming Methods The Effects of Noise Conclusions References Parallel Cooperating Genetic Algorithms: An Application to Robot Motion PlanningIntroduction Principles of Genetic Algorithms The Search Algorithm The Explore Algorithm The Ariadne’s CLEW Algorithm Parallel Implementation Conclusion, Results, and Perspective The Boltzmann Selection ProcedureIntroduction Empirical Analysis Introduction to Boltzmann Selection Theoretical Analysis Discussion and Related Work Conclusion Structure and Performance of Fine-Grain Parallelism in Genetic SearchIntroduction Three Fine