Fr. 346.00

Genetic Algorithms for Pattern Recognition

English · Hardback

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Description

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Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved.

List of contents

1.Fitness Evaluation in Genetic Algorithms with Ancestors' Influence 2. The Walsh Transform and the Theory of the Simple Genetic Algorithm 3. Adaptation in Genetic Algorithms 4. An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions 5. Generalization of Heuristics Learned in Genetics-Based Learning 6. Genetic Algorithm-Based Pattern Classification: Relationship with Bayes Classifier 7. Genetic Algorithms and Recognition Problems 8. Mesoscale Feature Labeling from Satellite Images 9. Learning to Learn with Evolutionary Growth Perceptrons 10. Genetic Programming of Logic-Based Neural Networks 11. Construction of Fuzzy Classification Systems with Linguistic If-Then Rules Using Genetic Algorithms 12. A Genetic Algorithm Method for Optimizing the Fuzzy Component of a Fuzzy Decision Tree 13. Genetic Design of Fuzzy Controllers. Index.

About the author










Sankar K. Pal, Paul P. Wang

Summary

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved.

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