Buch, Englisch, 448 Seiten, Format (B × H): 165 mm x 244 mm, Gewicht: 812 g
Buch, Englisch, 448 Seiten, Format (B × H): 165 mm x 244 mm, Gewicht: 812 g
ISBN: 978-0-8493-2529-8
Verlag: CRC Press
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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Professional
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Weitere Infos & Material
Contents
Introduction
Multi-Niche Crowding for Multi-modal Search
Introduction
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 Vehicle
Overview
Introduction to Artificial Neural Networks
Introduction to ALVINN
The Evolutionary Approach
Task Specifics
Implementation and Results
Conclusions
Future Directions
Locating Putative Protein Signal Sequences
Introduction
Implementation
Results of Sample Applications
Parametrization Study
Future Directions
Selection Methods for Evolutionary Algorithms
Fitness 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 Planning
Introduction
Principles of Genetic Algorithms
The Search Algorithm
The Explore Algorithm
The Ariadne’s CLEW Algorithm
Parallel Implementation
Conclusion, Results, and Perspective
The Boltzmann Selection Procedure
Introduction
Empirical Analysis
Introduction to Boltzmann Selection
Theoretical Analysis
Discussion and Related Work
Conclusion
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Introduction
Three Fine
