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• PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents.

• 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS.

• B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS;
• 1. Introduction; 1
• .1. Reminiscing; 1
• .2. The 1961 Model; 1
• .3. Notation;
• 2. Linear Separable NE; 2
• .1. Neuronic Equations; 2
• .2. Polygonal Inequalities; 2
• .3. Computation of the n-expansion of arbitrary l.s. functions; 2
• .4. Continuous versus discontinuous behaviour: transitions;
• 3. General Boolean NE; 3
• .1. Linearization in tensor space; 3
• .2. Next-state matrix; 3
• .3. Normal modes, attractors; 3
• .4. Synthesis of nets: the inverse problem; 3
• .5. Separable versus Boolean nets.

• Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS;
• 1. INTRODUCTION;
• 2. MODELING THE NOISY NEURON; 2
• .1. Empirical Properties of Neuron and Synapse;
• 22. Model of Shaw and Vasudevan; 2
• .3. Model of Little; 2
• .4. Model of Taylor.

• 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion
• Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS.

This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.
(source: Nielsen Book Data)

For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.
(source: Nielsen Book Data)
Naturally Intelligent Systems offers a comprehensive introduction to neural networks.
(source: Nielsen Book Data)
For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. From Mary Shelley's Frankenstein monster to the computer intelligence of HAL in 2001, scientists have been cast in the role of creator of such devices. Now there is a new entry into this arena, neural networks, and "Naturally Intelligent Systems explores these systems to see how they work and what they can do. Neural networks are not computers in any traditional sense, and they have little in common with earlier approaches to the problem of fabricating intelligent behavior. Instead, they are information processing systems that are physically modeled after the structure of the brain and that are "trained to perform a task rather than programmed like a computer. Neural networks, in fact, provide a tool with problemsolving capabilities - and limitations - strikingly similar to those of animals and people. In particular, they are successful in applications such as speech, vision, robotics, and pattern recognition. "Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks. No particular mathematical background is necessary; it is written for all interested readers. "Naturally Intelligent Systents clearly explains the underlying concepts of key neural network designs, how they are trained, and why they work. It compares their behavior to the natural intelligence found in animals - and people. Throughout, Caudill and Butler bring the field into focus by presenting actual applications that illustrate neural networks' utility in the real world. MaureenCaudill is President of Adaptics, a neural network consulting company in San Diego and author of the popular "Neural Network Primer" articles that appear regularly in "AI Expert. Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development. A Bradford Book.
(source: Nielsen Book Data)

• 1. Neural networks: hopes, problems, and goals
• 2. The loading problem
• 3. Other studies of learning
• 4. The intractability of loading
• 5. Subcases
• 6. Shallow architectures
• 7. Memorization and generalization
• 8. Conclusion.

Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.
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Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

• 1. Introduction and important definitions
• 2. Representation issues
• 3. Perceptron learning and the pocket algorithm
• 4. Winner-take-all groups or linear machines
• 5. Autoassociators and one-shot learning
• 6. Mean squared error (MSE) algorithms
• 7. Unsupervised learning
• 8. The distributed method and radial basis functions
• 9. Computational learning theory and the BRD algorithm
• 10. Constructive algorithms
• 11. Backpropagation
• 12. Backpropagation : variations and applications
• 13. Simulated annealing and boltzmann machines
• 14. Expert systems and neural networks
• 15. Details of the MACIE system
• 16. Noise, redundancy, fault detection, and bayesian decision theory
• 17. Extracting rules from networks.

Neural Network Learning and Expert Systems is the first book to present a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable for students and researchers in computer science, engineering, and psychology, this text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of neural network expert systems which shows how the power of neural network learning can be harnessed to generate expert systems automatically. Features include a comprehensive treatment of the standard learning algorithms (with many proofs), along with much original research on algorithms and expert systems. Additional chapters explore constructive algorithms, introduce computational learning theory, and focus on expert system applications to noisy and redundant problems. For students there is a large collection of exercises, as well as a series of programming projects that lead to an extensive neural network software package. All of the neural network models examined can be implemented using standard programming languages on a microcomputer.
(source: Nielsen Book Data)

• A perspective on the work in this book / Kenneth E. Kinnear, Jr.
• Introduction to genetic programming / John R. Koza
• The evolution of evolvability in genetic programming / Lee Altenberg
• Genetic programming and emergent intelligence / Peter J. Angeline
• Scalable learning in genetic programming using automatic function definition / John R. Koza
• Alternatives in automatic function definition : a comparison of performance / Kenneth E. Kinnear, Jr.
• The donut problem : scalability, generalization and breeding policies in genetic programming / Walter Alden Tackett, Aviram Carmi
• Effects of locality in individual and population evolution / Patrik D'haeseleer, Jason Bluming
• The evolution of mental models / Astro Teller
• Evolution of obstacle avoidance behavior : using noise to promote robust solutions / Craig W. Reynolds
• Pygmies and civil servants / Conor Ryan
• Genetic programming using a minimum decsription length principle / Hitoshi Iba, Hugo de Garis, Taisuke Sato
• Genetic programming in C++: implementation issues / Mike J. Keith, Martin C. Martin. A compiling genetic programming system that directly manipulates the machine code / Peter Nordin
• Automatic generation of programs for crawling and walking / Graham Spencer
• Genetic programming for the acquisition of double auction market strategies / Martin Andrews, Richard Prager
• Two scientific applications of genetic programming : stack filters and non-linear equation fitting to chaotic data / Howard Oakley
• The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions / Simon G. Handley
• Competitively evolving decision trees against fixed training cases for natural language processing / Eric V. Siegel
• Cracking and co-evolving randomizers / Jan Jannink
• Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand
• Evolvable 3D modeling for model-based object recognition systems / Thang Nguyen, Thomas Huang
• Automatically defined features : the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them / David Andre
• Genetic micro programming of neural networks / Frédéric Gruau.

Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.
(source: Nielsen Book Data)
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail. A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality. Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.
(source: Nielsen Book Data)

• Computers and computation
• the discrete neuron
• the Boolean neuron
• alternating circuits
• small, shallow alternating circuits
• threshold circuits
• cyclic networks
• probabilistic neural networks.
• (source: Nielsen Book Data)

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning.Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.
(source: Nielsen Book Data)
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.
(source: Nielsen Book Data)

• Fuzzy logic and genetic algorithms
• learning
• fuzzy and hybrid systems
• decision and aggregation techniques
• fuzzy logic in databases
• foundations of fuzzy logic
• applications of fuzzy sets.
• (source: Nielsen Book Data)

Soft computing is a new, emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. The principal components of soft computing are fuzzy logic, neurocomputing, genetic algorithms and probabilistic reasoning.This volume is a collection of up-to-date articles giving a snapshot of the current state of the field. It covers the whole expanse, from theoretical foundations to applications. The contributors are among the world leaders in the field.
(source: Nielsen Book Data)

• The Road to Intelligent Machines
• The Power and Limitations of Perceptrons
• Neural Logic Networks
• Probabilistic Neural Logic Networks
• Fuzzy Neural Logic Networks
• Temporal Neural Logic Networks
• Neural Logic Programming
• Connectionist Expert Systems
• Fuzzy Knowledge Processing
• The Art of Guessing.
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This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks which resemble the human intuition process.This book should appeal to researchers in artificial intelligence, neural computings and logic, as well as graduate and advance undergraduate students in computer science.
(source: Nielsen Book Data)

• Neural network performance metrics
• noise in neural implementations
• simulation requirements and environment
• fault tolerance
• generalisation ability
• learning trajectory and speed.
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Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a"fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.
(source: Nielsen Book Data)

• PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
• PART II: EXTENDING GENETIC ALGORITHMS.
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PART I: Comparison of Evolutionary Algorithms. 1. Organic Evolution and Problem Solving. 2. Specific Evolutionary Algorithms. 3. Artificial Landscapes. 4. An Empirical Comparison. PART II: Extending Genetic Algorithms. 5. Selection. 6. Mutation. 7. An Experiment in Meta-Evolution.
(source: Nielsen Book Data)
This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)

• 1. The faculty of knowledge engineering and problem solving
• 2. Knowledge engineering and symbolic artificial intelligence
• 3. From fuzzy sets to fuzzy systems
• 4. Neural networks : theoretical and computational models
• 5. Neural networks for knowledge engineering and problem solving
• 6. Hybrid symbolic, fuzzy, and connectionist systems : toward comprehensive artificial intelligence
• 7. Neural networks, fuzzy systems, and nonlinear dynamical systems. chaos ; toward new connectionist and fuzzy logic models.

"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems.".
(source: Nielsen Book Data)
Neural networks and fuzzy systems are different aprpoaches to introducing human-like reasoning into expert systems. This text combines the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particular feature of the text is that it is filled with applications in engineering, business and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering" has chapters structured for various levels of teaching and includes work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.
(source: Nielsen Book Data)

• Hardware and software aspects
• transputer topologies for parallel implementation
• comparison between serial and parallel implementation
• analysis and implementation for equal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a heterogeneous transputer array
• conclusion.
• (source: Nielsen Book Data)

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
(source: Nielsen Book Data)

• Fuzzy compensation principles
• normal fuzzy reasoning methodology
• compensatory genetic fuzzy neural networks
• fuzzy knowledge rediscovery in fuzzy rule bases
• fuzzy cat-pole balancing control systems
• fuzzy knowledge compression and expansion
• highly nonlinear system modelling and prediction
• fuzzy moves in fuzzy games
• genetic neuro-fuzzy pattern recognition
• constructive approach to modelling fuzzy systems.
• (source: Nielsen Book Data)

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
(source: Nielsen Book Data)

• Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al
• skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata
• a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al
• automatic fuzzy tuning and its applications, H. Ishigami et al
• an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al
• on-line self-structuring fuzzy inference systems for function approximation, H. Bersini
• fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang
• intelligent systems for fraud detection, J. Kingdon
• genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al
• fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al
• an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al
• a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.
• (source: Nielsen Book Data)

Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce the human-centered intelligent systems of tomorrow; the collection of papers presented in this book shows the way. The book also contains an extensive bibliography on fuzzy logic and genetic algorithms.
(source: Nielsen Book Data)

• Approximate reasoning about comlpex objects in distributed systems - rough mereological formulation
• FOOD - fuzzy object-oriented design
• approximating block access in database systems
• the computer zoo in a box
• expert system design
• automating creation of computer programs for design circuits using genetic programming
• self-organizing maps
• knowledge-based techniques for software quality management
• object networks in developing intelligent systems
• application of genetic programming in software quality prediction
• neural networks for software quality prediction
• fuzzy Petri nets and Choquet integral in software cost estimation
• non-Cartesian approach to software development
• inductive programming.
• (source: Nielsen Book Data)

This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering.The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts?With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography.
(source: Nielsen Book Data)

• Bianchini, Frasconi, Gori, and Maggini, Optimal Learning in Artificial Neural Networks: A Theoretical View. Kanjilal, Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems. Museli, Sequential Constructive Techniques. Yu, Xu, and Wang, Fast Backpropagation Training Using Optimal Learning Rate and Momentum. Angulo and Torras, Learning of Nonstationary Processes. Schaller, Constraint Satisfaction Problems. Yang and Chen, Dominant Neuron Techniques. Lin, Chiang, and Kim, CMAC-based Techniques for Adaptive Learning Control. Deco, Information Dynamics and Neural Techniques for Data Analysis. Gorinevsky, Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems.
• (source: Nielsen Book Data)

This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers, and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.
(source: Nielsen Book Data)

• Albertini and Pra, Recurrent Neural Networks: Identification and Other System Theoretic Properties. Anderson and Titterington, Boltzmann Machines: Statistical Associations and Algorithms for Training Anderson and Titterington. Campbell, Constructive Learning Techniques for Designing Neural Network Systems. Mehrotra and Mohan, Modular Neural Networks. Xu and Kwong, Associative Memories. Fry and Sova, A Logical Basis for Neural Network Design.Hoekstra, Duin, and Kraaijveld, Neural Networks Applied to Data Analysis. Zhang and Wang, Multi-Mode Single Neuron Arithmetics.
• (source: Nielsen Book Data)

Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.
(source: Nielsen Book Data)

• Contributors
• Acknowledgments
• 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, Una-May O'Reilly, Peter J. Angelino
• I. Applications
• 2. An Automatic Software Re-Engineering Tool Based on Genetic Programming / Conor Ryan and Laur Ivan
• 3. CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / Robert E. Keller, Wolfgang Banzhaf, Jorn Mehnen and Klaus Weinert
• 4. A Genetic Programming Approach for Robust Language Interpretation / Carolyn Penstein Rose
• 5. Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / Peter A. Whigham and Peter F. Crapper
• 6. Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / John R. Koza and Forest H. Bennett III
• 7. Quantum Computing Applications of Genetic Programming / Lee Spector, Howard Barnum, Herbert J. Bernstein and Nikhil Swamy
• II. Theory
• II. Theory
• 8. The Evolution of Size and Shape / William B. Langdon, Terry Soule, Riccardo Poli and James A. Foster
• 9. Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / Christian Igel and Kumar Chellapilla
• 10. Analysis of Single-Node (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
• 11. Rooted-Tree Schemata in Genetic Programming / Justinian P. Rosca and Dana H. Ballard
• III. Extensions
• III. Extensions
• 12. Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / Peter Nordin, Wolfgang Banzhaf and Frank D. Francone
• 13. Sub-machine-code Genetic Programming / Riccardo Poli and William B. Langdon
• 14. The Internal Reinforcement of Evolving Algorithms / Astro Teller
• 15. Inductive Genetic Programming with Immune Network Dynamics / Nikolay I. Nikolaev, Hitoshi Iba and Vanio Slavov
• 16. A Self-Tuning Mechanism for Depth-Dependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
• 17. Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / Geum Yong Lee
• 18. Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / Byoung-Tak Zhang and Dong-Yeon Cho
• 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
• Index.

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time -- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of "Advances in Genetic Programming" highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)