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Full Description
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. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems.
The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
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.
