Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.

Evolutionary Computation (International Series on Computational Intelligence)

Computers & Technology

Jain, Lakhmi C.

Dumitrescu, D.

Lazzerini, Beatrice

Dumitrescu, A.

Computer Science

CRC-Press

Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge in the last half-century. Its theoretical and technological promise is vast, and we are only beginning to experience its potential. Clustering is the first and most basic application of fuzzy set theory, but forms the basis of many, more sophisticated, intelligent computational models, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design.Fuzzy Sets and their Application to Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms.The authors - top experts from around the world - combine their talents to lay a solid foundation for applications of this powerful tool, from the basic concepts and mathematics through the study of various algorithms, to validity functionals and hierarchical clustering. The result is Fuzzy Sets and their Application to Clustering and Training - an outstanding initiation into the world of fuzzy learning classifiers and fuzzy clustering.

Fuzzy Sets & their Application to Clustering & Training (International Series on Computational Intelligence)

The successful development and deployment of expert system tools spurred the initial momentum in developing and using intelligent techniques in industry. The brittleness of expert systems and the enormous effort involved in the development and maintenance of knowledge bases prompted researchers to seek friendlier approaches. Neural networks, fuzzy logic, and evolutionary computing tools added a new dimension to the quest for more intelligent tools to supplement the capabilities of expert systems. In one volume, Knowledge-Based Intelligent Techniques in Industry comprehensively brings together the more important developments in the use of intelligent techniques in solving industrial problems. The book's primary readership includes electrical engineers in industry as well as researchers working in computational intelligence research labs - outlining state-of-the-art techniques and cost-effective solutions.Knowledge-Based Intelligent Techniques in Industry singularly reflects the increasing study of computational intelligence techniques for designing and monitoring complex, less predictable electrical or mechanical systems.

Knowledge-Based Intelligent Techniques in Industry (International Series on Computational Intelligence)

Johnson, R.P.

Takefuji, Yoshiyasu

Zadeh, Lofti A.

Fuzzy and Neuro-Fuzzy Systems in Medicineprovides a thorough review of state-of-the-art techniques and practices, defines and explains relevant problems, as well as provides solutions to these problems.After an introduction, the book progresses from one topic to another - with a linear development from fundamentals to applications.Chapters discuss:a historical perspective of fuzzy systems technology and neuro-fuzzy systems technology in medicine and biologythe relationship of fuzzy logic to the human brainanalysis and classification of signals using fuzzy, neuro-fuzzy, and wavelet methodswavelet analysis combined with neuro-fuzzy systems in contouring gated SPECT images of ventriclesa detailed application based on a knowledge-based system that uses fuzzy techniques, multispectral analysis, and image processing algorithmsapplications in the field of dentistrya dedicated system for myocardial ischemia diagnosisa typical expert system used in intensive care unitsdesigning and tuning fuzzy rules for medical diagnosisknowledge processing, decision-making, and control strategies combined with control methods in medical equipmentcurrent technological problems and trends in the neural and fuzzy hardware implementation fieldThe well-balanced chapters cover all the major fields in medicine and biomedical engineering, including imaging, prosthetics, psychology, medical equipment, diagnosis, and treatment.

Fuzzy and Neuro-Fuzzy Systems in Medicine (International Series on Computational Intelligence)

Kandel, Abraham

Teodorescu, Horia-Nicolai L

Software

This book presents the latest advances in remote-sensing and geographic information systems and applications. It is divided into four parts, focusing on Airborne Light Detection and Ranging (LiDAR) and Optical Measurements of Forests; Individual Tree Modelling; Landscape Scene Modelling; and Forest Eco-system Modelling. Given the scope of its coverage, the book offers a valuable resource for students, researchers, practitioners, and educators interested in remote sensing and geographic information systems and applications.

Handbook on Advances in Remote Sensing and Geographic Information Systems: Paradigms and Applications in Forest Landscape Modeling (Intelligent Systems Reference Library, 122)

Favorskaya, Margarita N.

Springer

Humans have always been hopeless at predicting the future…most people now generally agree that the margin of viability in prophecy appears to be 1 ten years. Even sophisticated research endeavours in this arena tend to go 2 off the rails after a decade or so. The computer industry has been particularly prone to bold (and often way off the mark) predictions, for example: ‘I think there is a world market for maybe five computers’ Thomas J. Watson, IBM Chairman (1943), ‘I have traveled the length and breadth of this country and talked with the best people, and I can assure you that data processing is a fad that won’t last out the year’ Prentice Hall Editor (1957), ‘There is no reason why anyone would want a computer in their home’ Ken Olsen, founder of DEC (1977) and ‘640K ought to be enough for anybody’ Bill Gates, CEO Microsoft (1981). 3 The field of Artificial Intelligence – right from its inception – has been particularly plagued by ‘bold prediction syndrome’, and often by leading practitioners who should know better. AI has received a lot of bad press 4 over the decades, and a lot of it deservedly so. How often have we groaned in despair at the latest ‘by the year-20xx, we will all have…(insert your own particular ‘hobby horse’ here – e. g.

Applied Intelligent Systems

Business & Money

Economics

Multiagent Systems and Applications: Volume 2: Development Using the GORITE BDI Framework (Intelligent Systems Reference Library, 46)

Jarvis, Jacqueline

Jarvis, Dennis

Rönnquist, Ralph

The Radial Basis Function (RBF) network has gained in popularity in recent years. This is due to its desirable properties in classification and functional approximation applications, accompanied by training that is more rapid than that of many other neural-network techniques. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of applications areas, for example, robotics, biomedical engineering, and the financial sector. The two-title series Theory and Applications of Radial Basis Function Networks provides a comprehensive survey of recent RBF network research. This volume, New Advances in Design, contains a wide range of applications in the laboratory and case-studies describing current use. The sister volume to this one, Recent Developments in Theory and Applications, covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms. The combination of the two volumes will prove extremely useful to practitioners in the field, engineers, researchers, students and technically accomplished managers.

Radial Basis Function Networks 2: New Advances in Design (Studies in Fuzziness and Soft Computing, 67)

Howlett, Robert J.

Physica

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

AUTOMATIC GENERATION OF NEURAL NETWORK ARCHITECTURE USING EVOLUTIONARY COMPUTATION (Advances in Fuzzy Systems-Applications and Theory)

Vonk, E

Certification

World Scientific Publishing Company

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