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1. Evolution, learning, and cognition [1988]
 Singapore ; Teaneck, N.J., USA : World Scientific, ©1988.
 Description
 Book — 1 online resource (x, 411 pages) : illustrations
 Summary

 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; BACKPROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended BackPropagation; 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 McCULLOCHPITTS; 2.1 StateTheoretic Description; 2.2 Associative Memory; 3 THE OUTERPRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 OuterProducts Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS.
 B OUTERPRODUCT 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 nexpansion of arbitrary l.s. functions; 2
 .4. Continuous versus discontinuous behaviour: transitions;
 3. General Boolean NE; 3
 .1. Linearization in tensor space; 3
 .2. Nextstate 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 HyperplaneDirected 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 2NEURON NETWORKS.
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2. Naturally intelligent systems [1990]
 Caudill, Maureen.
 Cambridge, Mass. : MIT Press, ©1990.
 Description
 Book — 1 online resource (304 pages) : illustrations
 Summary

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 downtoearth 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 downtoearth 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)
 Judd, J. Stephen.
 Cambridge, Mass. : MIT Press, ©1990.
 Description
 Book — 1 online resource (150 pages) : illustrations
 Summary

 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.
(source: Nielsen Book Data)
 Neurale netværk. English
 Brunak, Søren.
 Singapore ; Teaneck, N.J., USA : World Scientific, ©1990.
 Description
 Book — 1 online resource (180 pages) : illustrations
 Summary

Both specialists and laymen will enjoy reading this book. Using a lively, nontechnical 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)
 Gallant, Stephen I.
 Cambridge, Mass. : MIT Press, ©1993.
 Description
 Book — 1 online resource (xvi, 365 pages) : illustrations
 Summary

 1. Introduction and important definitions
 2. Representation issues
 3. Perceptron learning and the pocket algorithm
 4. Winnertakeall groups or linear machines
 5. Autoassociators and oneshot 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.
(source: Nielsen Book Data)
 Cambridge, Massachusetts : The MIT Press, [1994]
 Description
 Book — 1 online resource (ix, 476 pages) : illustrations
 Summary

 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 nonlinear 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 coevolving randomizers / Jan Jannink
 Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand
 Evolvable 3D modeling for modelbased object recognition systems / Thang Nguyen, Thomas Huang
 Automatically defined features : the simultaneous evolution of 2dimensional feature detectors and an algorithm for using them / David Andre
 Genetic micro programming of neural networks / Frédéric Gruau.
(source: Nielsen Book Data)
There is increasing interest in genetic programming by both researchers and professional software developers. These twentytwo 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)
7. Circuit complexity and neural networks [1994]
 Parberry, Ian.
 Cambridge, Mass. : MIT Press, ©1994.
 Description
 Book — 1 online resource (xxix, 270 pages) : illustrations
 Summary

 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)
(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)
8. Fuzzy logic and soft computing [1995]
 Singapore ; River Edge, NJ : World Scientific, ©1995.
 Description
 Book — 1 online resource (x, 497 pages) : illustrations
 Summary

 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)
(source: Nielsen Book Data)
 Teh, H. H.
 Singapore ; River Edge, NJ : World Scientific, ©1995.
 Description
 Book — 1 online resource (xv, 504 pages) : illustrations
 Summary

 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
10. Analogue imprecision in MLP training [1996]
 Edwards, Peter J. (Peter John)
 Singapore ; River Edge, NJ : World Scientific, ©1996.
 Description
 Book — 1 online resource (xi, 178 pages) : illustrations
 Summary

 Neural network performance metrics
 noise in neural implementations
 simulation requirements and environment
 fault tolerance
 generalisation ability
 learning trajectory and speed.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Bäck, Thomas, 1963
 New York : Oxford University Press, 1996.
 Description
 Book — 1 online resource (xii, 314 pages) : illustrations
 Summary

 PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
 PART II: EXTENDING GENETIC ALGORITHMS.
 (source: Nielsen Book Data)
(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 metaevolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)
 Kasabov, Nikola K.
 Cambridge, Mass. : MIT Press, ©1996.
 Description
 Book — 1 online resource (xvi, 550 pages) : illustrations Digital: text file; PDF.
 Summary

 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.
(source: Nielsen Book Data)
Neural networks and fuzzy systems are different aprpoaches to introducing humanlike 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 rulebased 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 applicationoriented 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)
 Saratchandran, P.
 Singapore ; River Edge, NJ : World Scientific, ©1996.
 Description
 Book — 1 online resource (xviii, 202 pages)
 Summary

 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)
(source: Nielsen Book Data)
 Zhang, YanQing.
 Singapore ; River Edge, N.J. : World Scientific, 1997.
 Description
 Book — 1 online resource (xii, 186 pages) : illustrations
 Summary

 Fuzzy compensation principles
 normal fuzzy reasoning methodology
 compensatory genetic fuzzy neural networks
 fuzzy knowledge rediscovery in fuzzy rule bases
 fuzzy catpole balancing control systems
 fuzzy knowledge compression and expansion
 highly nonlinear system modelling and prediction
 fuzzy moves in fuzzy games
 genetic neurofuzzy pattern recognition
 constructive approach to modelling fuzzy systems.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Singapore ; River Edge, NJ : World Scientific Pub., ©1997.
 Description
 Book — 1 online resource (x, 240 pages) : illustrations
 Summary

 Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al
 skill acquisition and skillbased 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 SGSThomson's W.A.R.P. fuzzy processor, R. Poluzzi et al
 online selfstructuring 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 logicgenetic algorithms bibliography  19891995, O. Cordon et al.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
16. Machine vision [1997]
 Norwood, N.J. : Ablex Pub. Corp., ©1997.
 Description
 Book — 1 online resource (vii, 305 pages) : illustrations
 Singapore ; River Edge, N.J. : World Scientific, ©1998.
 Description
 Book — 1 online resource (xiii, 485 pages) : illustrations
 Summary

 Approximate reasoning about comlpex objects in distributed systems  rough mereological formulation
 FOOD  fuzzy objectoriented 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
 selforganizing maps
 knowledgebased 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
 nonCartesian approach to software development
 inductive programming.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
18. Implementation techniques [1998]
 San Diego, Calif. : Academic Press, ©1998.
 Description
 Book — 1 online resource (xviii, 401 pages) : illustrations
 Summary

 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, CMACbased Techniques for Adaptive Learning Control. Deco, Information Dynamics and Neural Techniques for Data Analysis. Gorinevsky, Radial Basis Function Network Approximation and Learning in TaskDependent Feedforward Control of Nonlinear Dynamical Systems.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
19. Optimization techniques [1998]
 Leondes, Cornelius T.
 San Diego : Academic Press, 1998.
 Description
 Book — 1 online resource (xxii, 398 pages) : illustrations
 Summary

 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, MultiMode Single Neuron Arithmetics.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
20. Advances in genetic programming. Volume III [1999]
 Cambridge, Mass. : MIT Press, [1999]
 Description
 Book — 1 online resource (476 pages) : illustrations
 Summary

 Contributors
 Acknowledgments
 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, UnaMay O'Reilly, Peter J. Angelino
 I. Applications
 2. An Automatic Software ReEngineering 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 RainfallRunoff 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 SingleNode (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
 11. RootedTree 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. Submachinecode 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 SelfTuning Mechanism for DepthDependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
 17. Genetic Recursive Regression for Modeling and Forecasting RealWorld Chaotic Time Series / Geum Yong Lee
 18. Coevolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / ByoungTak Zhang and DongYeon Cho
 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
 Index.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and programlike executable structures for developing reliable time  and costeffective 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)
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