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.
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