Revival: Genetic Algorithms for Pattern Recognition (1986)

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.

Fitness Evaluation in Genetic Algorithms with Ancestors' Influence, S. De, A. Ghosh, and S.K. PalThe Walsh Transform and the Theory of the Simple Genetic Algorithm, M.D. Vose and A.H. WrightAdaptation in Genetic Algorithms, L.M. Patnaik and M. SrinivasAn Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions, K. Mathias, D. Whitley, A. Kusuma, and C. StorkGeneralization of Heuristics Learned in Genetics-Based Learning, B.W. Wah, A. Ieumwananonthachai, and Y.-C. LiGenetic Algorithm-Based Pattern Classification: Relationship with Bayes Classifier, C.A. Murthy, S. Bandyopadhyay, and S.K. PalGenetic Algorithms and Recognition Problems, H. Van Hove and A. VerschorenMesoscale Feature Labeling from Satellite Images, B.P. Buckles, F.E. Petry, D. Prabhu, and M. LybanonLearning to Learn with Evolutionary Growth Perceptrons, S.G. RomaniukGenetic Programming of Logic-Based Neural Networks, V.C. GaudetConstruction of Fuzzy Classification Systems with Linguistic If-Then Rules Using Genetic Algorithms, H. Ishibuchik, T. Murata, and H. TanakaA Genetic Algorithm Method for Optimizing the Fuzzy Component of a Fuzzy Decision Tree, C.Z. JanikowGenetic Design of Fuzzy Controllers, M.G. Cooper and J.J. VidalIndex

Sankar Kumar Pal is a Distinguished Scientist and former Director of the Indian Statistical Institute, Kolkata, India. He is a computer scientist with an international reputation on fuzzy neural network, soft computing, and machine intelligence. He founded the Machine Intelligence Unit in 1993, and the Center for Soft Computing Research: A National Facility in 2004, both at the ISI. He is the founder President of the Indian National Academy of Engineering, Kolkata Chapter