Computational intelligence - concepts to implementations

Russ Eberhart and Yuhui Shi have succeeded in integrating various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook, including lots of practical examples. -Shun-ichi Amari, RIKEN Brain Science Institute, Japan This book is an excellent choice on its own, but, as in my case, will form the foundation for our advanced graduate courses in the CI disciplines. -James M. Keller, University of Missouri-Columbia The excellent new book by Eberhart and Shi asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. The book has an emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. -Xin Yao, The Centre of Excellence for Research in Computational Intelligence and Applications, Birmingham The "soft" analytic tools that comprise the field of computational intelligence have matured to the extent that they can, often in powerful combination with one another, form the foundation for a variety of solutions suitable for use by domain experts without extensive programming experience. Computational Intelligence: Concepts to Implementations provides the conceptual and practical knowledge necessary to develop solutions of this kind. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. Features · Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies. · Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. · Details the metrics and analytical tools needed to assess the performance of computational intelligence tools. · Concludes with a series of case studies that illustrate a wide range of successful applications. · Presents code examples in C and C++. · Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study. · Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications. · Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies. · Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. · Details the metrics and analytical tools needed to assess the performance of computational intelligence tools. · Concludes with a series of case studies that illustrate a wide range of successful applications. · Presents code examples in C and C++. · Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study. · Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications.

[1]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  Thomas Bäck,et al.  Generalized Convergence Models for Tournament- and (mu, lambda)-Selection , 1995, ICGA.

[3]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[4]  Roger Sauter,et al.  Introduction to Probability and Statistics for Engineers and Scientists , 2005, Technometrics.

[5]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[6]  Alex Fraser.  Simulation of genetic systems , 1962 .

[7]  Xin Yao.  The Evolution of Evolutionary Computation , 2003, KES.

[8]  Peter J. B. Hancock,et al.  Genetic algorithms and permutation problems: a comparison of recombination operators for neural net structure specification , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[9]  Chyck Karr,et al.  Applying genetics to fuzzy logic , 1991 .

[10]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[11]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[12]  Shun-ichi Amari,et al.  Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements , 1972, IEEE Transactions on Computers.

[13]  B. Ebanks.  On measures of fuzziness and their representations , 1983 .

[14]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[15]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[16]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[17]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[18]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[19]  S. Pal,et al.  Object-background segmentation using new definitions of entropy , 1989 .

[20]  James A. Anderson,et al.  Neurocomputing: Foundations of Research , 1988 .

[21]  J. Bezdek,et al.  Fuzzy partitions and relations; an axiomatic basis for clustering , 1978 .

[22]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[23]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[24]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[25]  Centor Rm,et al.  Receiver Operating Characteristics (ROC) Curve Area Analysis Using the ROC ANALYZER. , 1989 .

[26]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .

[27]  James C. Bezdek,et al.  On the relationship between neural networks, pattern recognition and intelligence , 1992, Int. J. Approx. Reason..

[28]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[29]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[30]  Maureen Caudill,et al.  Neural networks primer, part III , 1988 .

[31]  M L Meistrell,et al.  Evaluation of neural network performance by receiver operating characteristic (ROC) analysis: examples from the biotechnology domain. , 1989, Computer methods and programs in biomedicine.

[32]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[33]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[34]  Dwight Goddard,et al.  A Buddhist Bible , 1994 .

[35]  Jacek M. Zurada,et al.  Sensitivity analysis for minimization of input data dimension for feedforward neural network , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[36]  A G Barto,et al.  Learning by statistical cooperation of self-interested neuron-like computing elements. , 1985, Human neurobiology.

[37]  Enrique H. Ruspini.  New experimental results in fuzzy clustering , 1973, Inf. Sci..

[38]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[39]  H. Szu.  Fast simulated annealing , 1987 .

[40]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[41]  Fernando J. Pineda,et al.  Dynamics and architecture for neural computation , 1988, J. Complex..

[42]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[43]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[44]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[45]  D. F. Specht,et al.  Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[46]  Michael N. Vrahatis,et al.  Tuning PSO Parameters Through Sensitivity Analysis , 2002 .

[47]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[48]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[49]  B. Latané.  The psychology of social impact. , 1981 .

[50]  Nelson Morgan,et al.  Statistical Pattern Classification , 1994 .

[51]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[52]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[53]  J. Kennedy.  Thinking is Social , 1998 .

[54]  David B. Fogel.  What is evolutionary computation , 1995 .

[55]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[56]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[57]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[58]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[59]  W R Webber,et al.  Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. , 1994, Electroencephalography and clinical neurophysiology.

[60]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[61]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[62]  Hector J. Levesque,et al.  Knowledge Representation and Reasoning , 2004 .

[63]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[64]  Shun-ichi Amari,et al.  Field theory of self-organizing neural nets , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[65]  Peter J. F. Lucas,et al.  Principles of expert systems , 1991, International computer science series.

[66]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[67]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[68]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[69]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[70]  A G Barto,et al.  Simulation Experiments with Goal-Seeking Adaptive Elements. , 1984 .

[71]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[72]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[73]  Jude W. Shavlik,et al.  Learning Symbolic Rules Using Artificial Neural Networks , 1993, ICML.

[74]  Robert J. Marks,et al.  Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter , 1995, IEEE Trans. Neural Networks.

[75]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[76]  Yuhui Shi,et al.  Co-evolutionary particle swarm optimization to solve min-max problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[77]  John Archibald Wheeler,et al.  At Home in the Universe , 1994 .

[78]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[79]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

[80]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[81]  Bernard Widrow,et al.  Neural nets for adaptive filtering and adaptive pattern recognition , 1988, Computer.

[82]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[83]  J. Haldane,et al.  The Causes of Evolution , 1933 .

[84]  Kunihiko Fukushima,et al.  A neural network model for selective attention in visual pattern recognition , 1986, Biological Cybernetics.

[85]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[86]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[87]  Teuvo Kohonen,et al.  A Simple Paradigm for the Self-Organized Formation of Structured Feature Maps , 1982 .

[88]  W. E. Thompson,et al.  Design of intelligent fuzzy logic controllers using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[89]  K. Adlassnig,et al.  Performance evaluation of medical expert systems using ROC curves. , 1989, Computers and biomedical research, an international journal.

[90]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[91]  W. A. Clark,et al.  Simulation of self-organizing systems by digital computer , 1954, Trans. IRE Prof. Group Inf. Theory.

[92]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[93]  Xin Yao,et al.  Evolutionary Artificial Neural Networks , 1993, Int. J. Neural Syst..

[94]  W. Ashby,et al.  Principles of the self-organizing dynamic system. , 1947, The Journal of general psychology.

[95]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[96]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[97]  Lotfi A. Zadeh,et al.  Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems , 1998 .

[98]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[99]  David E. Goldberg,et al.  Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization , 1987, Engineering with Computers.

[100]  Jose C. Principe,et al.  Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM , 1999 .

[101]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.

[102]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[103]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[104]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[105]  S. Amari,et al.  Mathematical theory on formation of category detecting nerve cells , 1978, Biological Cybernetics.

[106]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[107]  James C. Bezdek,et al.  Computational Intelligence Defined - By Everyone ! , 1998 .

[108]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[109]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[110]  Donald O. Walter,et al.  Self-Organizing Systems , 1987, Life Science Monographs.

[111]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[112]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[113]  Patrick K. Simpson,et al.  Fuzzy neural network machine prognosis , 1995, Defense, Security, and Sensing.

[114]  J. Swets.  Signal detection and recognition by human observers : contemporary readings , 1964 .

[115]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[116]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[117]  W. Ashby,et al.  The Physical Origin of Adaptation by Trial and Error , 1945 .

[118]  D F Specht,et al.  Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition. , 1967, IEEE transactions on bio-medical engineering.

[119]  Min-Jea Tahk,et al.  Coevolutionary augmented Lagrangian methods for constrained optimization , 2000, IEEE Trans. Evol. Comput..

[120]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[121]  S. Levy.  Artificial life: the quest for a new creation , 1992 .

[122]  S. Grossberg.  Neural pattern discrimination. , 1970, Journal of theoretical biology.

[123]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[124]  Douglas F. Elliott,et al.  Handbook of Digital Signal Processing: Engineering Applications , 1988 .

[125]  Donald F. Specht,et al.  Generation of Polynomial Discriminant Functions for Pattern Recognition , 1967, IEEE Trans. Electron. Comput..

[126]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[127]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[128]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[129]  A. Fraser.  Simulation of Genetic Systems by Automatic Digital Computers VI. Epistasis , 1960 .

[130]  Shun-ichi Amari,et al.  Characteristics of randomly connected threshold-element networks and network systems , 1971 .

[131]  Gilbert Syswerda,et al.  The Application of Genetic Algorithms to Resource Scheduling , 1991, International Conference on Genetic Algorithms.

[132]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[133]  David H. Sharp,et al.  Neural nets and artificial intelligence , 1989 .

[134]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[135]  M. Black.  Vagueness. An Exercise in Logical Analysis , 1937, Philosophy of Science.

[136]  Murray R. Spiegel,et al.  Schaum's Outline of Theory and Problems of Probability and Statistics , 1980 .

[137]  George Dyson,et al.  Darwin among the machines , 1998, The Mathematical Gazette.

[138]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[139]  William F. Allman.  Apprentices of Wonder: Inside the Neural Network Revolution , 1989 .

[140]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[141]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[142]  L. Darrell Whitley,et al.  Genetic Reinforcement Learning with Multilayer Neural Networks , 1991, ICGA.

[143]  Michio Sugeno,et al.  Applied fuzzy systems , 1994 .

[144]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[145]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[146]  Mark E. Oxley,et al.  Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[147]  S. Grossberg.  Neural Networks and Natural Intelligence , 1988 .

[148]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[149]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[150]  J.A. Anderson,et al.  Directions for research , 1990 .

[151]  D. G. Jr. Lee.  Preliminary results of applying neural networks to ship image recognition , 1989, International 1989 Joint Conference on Neural Networks.

[152]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[153]  Stanley C. Ahalt,et al.  Competitive learning algorithms for vector quantization , 1990, Neural Networks.

[154]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[155]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[156]  T. P. Caudell.  Parametric connectivity: feasibility of learning in constrained weight space , 1989, International 1989 Joint Conference on Neural Networks.

[157]  Marco Dorigo,et al.  From Natural to Artificial Swarm Intelligence , 1999 .

[158]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[159]  David S. Moore,et al.  Statistics: Concepts and Controversies , 1979 .

[160]  R. Bellman.  Dynamic programming. , 1957, Science.

[161]  S.-I. Amari,et al.  Neural theory of association and concept-formation , 1977, Biological Cybernetics.

[162]  Larry J. Eshelman,et al.  Using genetic search to exploit the emergent behavior of neural networks , 1990 .

[163]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[164]  R. E. Uhrig,et al.  Sensitivity analysis and applications to nuclear power plant , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[165]  Bart Kosko,et al.  Fuzzy entropy and conditioning , 1986, Inf. Sci..

[166]  Kristin P. Bennett,et al.  Stripmining For Molecules , 2002 .

[167]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[168]  James L. McClelland,et al.  Explorations in parallel distributed processing: a handbook of models, programs, and exercises , 1988 .

[169]  Ralf Bruns,et al.  Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling , 1993, ICGA.

[170]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[171]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[172]  Peter J. Bentley,et al.  CREATIVE EVOLUTIONARY SYSTEMS , 2001 .

[173]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[174]  Charles W. Butler,et al.  Naturally intelligent systems , 1990 .

[175]  C. L. Karr,et al.  Fuzzy control of pH using genetic algorithms , 1993, IEEE Trans. Fuzzy Syst..

[176]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[177]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[178]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[179]  R. Nakano,et al.  Medical diagnostic expert system based on PDP model , 1988, IEEE 1988 International Conference on Neural Networks.

[180]  W. Wee.  On generalizations of adaptive algorithms and application of the fuzzy sets concept to pattern classification , 1967 .

[181]  R. Axelrod.  Effective Choice in the Prisoner's Dilemma , 1980 .

[182]  Earl Cox,et al.  The Fuzzy Systems Handkbook with Cdrom , 1998 .

[183]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[184]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[185]  Craig Stanfill,et al.  Parallel free-text search on the connection machine system , 1986, CACM.

[186]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[187]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[188]  Russell C. Eberhart,et al.  Designing neural network explanation facilities using genetic algorithms , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[189]  C. Malsburg.  Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[190]  King-Sun Fu,et al.  A Formulation of Fuzzy Automata and Its Application as a Model of Learning Systems , 1969, IEEE Trans. Syst. Sci. Cybern..

[191]  Russell C. Eberhart,et al.  CaseNet: a neural network tool for EEG waveform classification , 1989, [1989] Proceedings. Second Annual IEEE Symposium on Computer-based Medical Systems.

[192]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[193]  Stephen Wolfram,et al.  Cellular Automata And Complexity , 1994 .

[194]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[195]  Arthur P. Mange,et al.  Basic Human Genetics , 1993 .

[196]  Darrell Whitley,et al.  Applying genetic algorithms to neural network learning , 1989 .

[197]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[198]  James C. Bezdek,et al.  Measuring fuzzy uncertainty , 1994, IEEE Trans. Fuzzy Syst..

[199]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[200]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[201]  Frederick C. Mish.  Merriam Webster's Collegiate Dictionary , 1998 .

[202]  Paul Harmon,et al.  Creating Expert Systems for Business and Industry , 1990 .

[203]  Russell C. Eberhart.  Standardization of neural network terminology , 1990, IEEE Trans. Neural Networks.

[204]  Halbert White,et al.  Neural-network learning and statistics , 1989 .

[205]  José L. Verdegay,et al.  The use of fuzzy connectives to design real-coded genetic algorithms , 1994 .

[206]  Stephen Grossberg,et al.  Contour Enhancement, Short Term Memory, and Constancies in Reverberating Neural Networks , 1973 .

[207]  H. Zimmermann,et al.  Decisions and evaluations by hierarchical aggregation of information , 1983 .

[208]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[209]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[210]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

[211]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[212]  L. Zadeh.  A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .

[213]  R. M. Kil,et al.  Bidirectional continuous associator based on Gaussian potential function network , 1989, International 1989 Joint Conference on Neural Networks.

[214]  H. Zimmermann,et al.  Latent connectives in human decision making , 1980 .

[215]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[216]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[217]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[218]  Richard M. Friedberg,et al.  A Learning Machine: Part I , 1958, IBM J. Res. Dev..

[219]  D. McClish,et al.  Comparing the Areas under More Than Two Independent ROC Curves , 1987, Medical decision making : an international journal of the Society for Medical Decision Making.

[220]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[221]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[222]  Peter Ross,et al.  A Genetic Algorithm for Job-Shop Problems with Various Schedule Quality Criteria , 1996, Evolutionary Computing, AISB Workshop.

[223]  R. C. Eberhart,et al.  The role of genetic algorithms in neural network query-based learning and explanation facilities , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.