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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

Pattern perception. --- Machine learning. --- Genetic algorithms. --- Automatic classification. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Learning, Machine --- Artificial intelligence --- Machine theory --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Automatic indexing --- Classification --- Information storage and retrieval systems --- Optical pattern recognition. --- Computer science. --- Telecommunication. --- Artificial intelligence. --- Bioinformatics. --- Pattern Recognition. --- Programming Techniques. --- Communications Engineering, Networks. --- Artificial Intelligence. --- Complex Systems. --- Computational Biology/Bioinformatics. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Electric communication --- Mass communication --- Telecom --- Telecommunication industry --- Telecommunications --- Communication --- Information theory --- Telecommuting --- Informatics --- Science --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data processing --- Pattern recognition. --- Computer programming. --- Electrical engineering. --- Statistical physics. --- Dynamical systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Electric engineering --- Engineering --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Statistical methods --- Programming --- Pattern recognition systems. --- System theory. --- Automated Pattern Recognition. --- Computational and Systems Biology. --- Systems, Theory of --- Systems science --- Pattern classification systems --- Pattern recognition computers --- Computer vision --- Philosophy

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Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in

Neural networks (Computer science) --- Systolic array circuits --- Array circuits, Systolic --- Circuits, Systolic array --- Systolic arrays --- Integrated circuits --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Design and construction. --- Very large scale integration --- Wafer-scale integration

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Mathematical statistics --- Molecular biology --- Computer science --- Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- patroonherkenning --- factoranalyse --- toegepaste informatica --- bio-informatica --- database management --- KI (kunstmatige intelligentie) --- robots --- moleculaire biologie --- AI (artificiële intelligentie)

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681.3*I2 --- Artificial intelligence --- Case-based reasoning. --- Soft computing. --- Agrotechnology and Food Sciences. Engineering --- Engineering (General) --- Engineering (General). --- Case-based reasoning --- Soft computing --- Cognitive computing --- Electronic data processing --- Computational intelligence --- Case-based learning --- Reasoning

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Artificial intelligence. Robotics. Simulation. Graphics