Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples.Key features:Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB exercises and applications in each chapterPresents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systemsConsiders real world problems in the domain of systems modelling, control and optimizationContains a foreword written by Lotfi ZadehComputational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

[1]  J. R. Knisley,et al.  A Darwinian approach to artificial neural systems , 1987 .

[2]  Zi-Xing Cai,et al.  Intelligent Control: Principles, Techniques and Applications , 1997, Series in Intelligent Control and Intelligent Automation.

[3]  Shiro Usui,et al.  Mutation-based genetic neural network , 2005, IEEE Transactions on Neural Networks.

[4]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[5]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[6]  Gary B. Lamont,et al.  Multiobjective optimization with messy genetic algorithms , 2000, SAC '00.

[7]  Christopher M. Brown,et al.  Parallel genetic algorithms on distributed-memory architectures , 1993 .

[8]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[9]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[10]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[11]  Richard S. Sutton,et al.  A Bioreactor Benchmark for Adaptive Network-based Process Control , 1995 .

[12]  D. Nauck,et al.  Neuro-Fuzzy Classification with NEFCLASS , 1996 .

[13]  Emanuele Della Valle,et al.  Classification and Clustering , 2021, Foundations of Statistics for Data Scientists.

[14]  Aviv Bergman,et al.  BREEDING INTELLIGENT AUTOMATA. , 1987 .

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

[16]  Patricia Melin,et al.  Parallel genetic algorithms for optimization of Modular Neural Networks in pattern recognition , 2011, The 2011 International Joint Conference on Neural Networks.

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

[18]  Hans Henrik Thodberg,et al.  Improving Generalization of Neural Networks Through Pruning , 1991, Int. J. Neural Syst..

[19]  Marzuki Khalid,et al.  Temperature regulation with neural networks and alternative control schemes , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[20]  Salmiah Ahmad,et al.  Evolutionary Tuning of Modular Fuzzy Controller for Two-wheeled Wheelchair , 2012, Int. J. Comput. Intell. Appl..

[21]  H. C. Card,et al.  Linguistic interpretation of self-organizing maps , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[22]  David R. Jefferson,et al.  An Artificial Neural Network Representation for Artificial Organisms , 1990, PPSN.

[23]  M. O. Tokhi,et al.  Training neural networks: backpropagation vs. genetic algorithms , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[24]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[25]  Hyun-Joon Cho,et al.  Fuzzy-PID hybrid control: Automatic rule generation using genetic algorithms , 1997, Fuzzy Sets Syst..

[26]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[27]  Kotaro Hirasawa,et al.  A study of evolutionary multiagent models based on symbiosis , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Panos Liatsis,et al.  A collective-based adaptive symbiotic model for surface reconstruction in area-based stereo , 2003, IEEE Trans. Evol. Comput..

[29]  Kenneth DeJong,et al.  Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).

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

[31]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[32]  Stefan Bornholdt,et al.  General asymmetric neural networks and structure design by genetic algorithms: a learning rule for temporal patterns , 1992, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[33]  D. Nauck,et al.  Nefclass | a Neuro{fuzzy Approach for the Classification of Data , 1995 .

[34]  Chun-Hee Woo,et al.  Evolutionary design of fuzzy rule base for nonlinear system modeling and control , 2000, IEEE Trans. Fuzzy Syst..

[35]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[36]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[37]  Jordan B. Pollack,et al.  Symbiotic Composition and Evolvability , 2001, ECAL.

[38]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[39]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[40]  K. De Jong Adaptive System Design: A Genetic Approach , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[41]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[42]  Lakhmi C. Jain,et al.  Automatic Generation of Neural Network Architecture Using Evolutionary Computation , 1997, Advances in Fuzzy Systems - Applications and Theory.

[43]  Robert G. Reynolds,et al.  Cultural Evolution of Ensemble Learning for Problem Solving , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[44]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[45]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

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

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

[48]  T. C. Chin,et al.  Genetic algorithms for learning the rule base of fuzzy logic controller , 1998, Fuzzy Sets Syst..

[49]  Renren Liu,et al.  Tuning of the Structure and Parameters of a Neural Network Using a Hybrid Good Point Set Evolutionary Strategy , 2009, 2009 Fifth International Conference on Natural Computation.

[50]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[51]  Alberto Tesi,et al.  On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[53]  Hong Wang,et al.  A direct adaptive neural network control for unknown nonlinear systems and its application , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[54]  Yong Yang,et al.  A Coarse-Grained Parallel Genetic Algorithm Employing Cluster Analysis for Multi-modal Numerical Optimisation , 2003, Artificial Evolution.

[55]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[56]  Hideyuki Takagi,et al.  Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms , 1997 .

[57]  Kumpati S. Narendra,et al.  Adaptation and learning in automatic systems , 1974 .

[58]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognition Letters.

[59]  Francisco Herrera,et al.  Fuzzy connectives based crossover operators to model genetic algorithms population diversity , 1997, Fuzzy Sets Syst..

[60]  Yukinori Kakazu,et al.  An Approach to the Analysis of the Basins of the Associative Memory Model Using Genetic Algorithms , 1991, ICGA.

[61]  [2] K.J. ˚Aström and B. Wittenmark. On self-tuning regulators. Automatica,9:185–199, , .

[62]  Ivan Sekaj,et al.  Some aspects of parallel genetic algorithms with population re-initialization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[63]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[64]  E. Cantu-Paz,et al.  An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[65]  Kenneth A. De Jong,et al.  An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms , 1990, PPSN.

[66]  Jordan B. Pollack,et al.  How Symbiosis Can Guide Evolution , 1999, ECAL.

[67]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[68]  V. Braitenberg Vehicles, Experiments in Synthetic Psychology , 1984 .

[69]  Thomas P. Caudell,et al.  Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms , 1989, ICGA.

[70]  Christian Igel,et al.  Operator adaptation in evolutionary computation and its application to structure optimization of neural networks , 2003, Neurocomputing.

[71]  A. Titli,et al.  Fuzzy logic control compared with other automatic control approaches , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[72]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[73]  Terence D. Sanger,et al.  A tree-structured adaptive network for function approximation in high-dimensional spaces , 1991, IEEE Trans. Neural Networks.

[74]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

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

[76]  Kendall E. Nygard,et al.  Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters , 1990, ML.

[77]  Kenneth A. De Jong,et al.  Evolving Complex Structures via Cooperative Coevolution , 1995, Evolutionary Programming.

[78]  Manfred Glesner,et al.  Fast Perceptron Learning by Fuzzy Controlled Dynamic Adaptation of Network Parameters , 1994 .

[79]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[80]  Y. H. Pao,et al.  Characteristics of the functional link net: a higher order delta rule net , 1988, IEEE 1988 International Conference on Neural Networks.

[81]  Arputharaj Kannan,et al.  A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system , 2006, Soft Comput..

[82]  Sio Carlos,et al.  Evolving a learning algorithm for the binary perceptron , 1991 .

[83]  Julian Morris,et al.  Applications of dynamic artificial neural networks in state estimation and nonlinear process control , 1995 .

[84]  Paul Juell,et al.  Integration of adaptive machine learning and knowledge-based systems for routing and scheduling applications , 1991 .

[85]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[86]  J. David Schaffer,et al.  Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms , 1988, ML.

[87]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[88]  Eiji Mizutani,et al.  Coactive neural fuzzy modeling , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[89]  Hitoshi Iba,et al.  Regularization approach to inductive genetic programming , 2001, IEEE Trans. Evol. Comput..

[90]  Xin Yao,et al.  Evolutionary design of artificial neural networks with different nodes , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[91]  K. S. Narendra,et al.  Neural networks for control theory and practice , 1996, Proc. IEEE.

[92]  D. R. McGregor,et al.  Designing application-specific neural networks using the structured genetic algorithm , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[93]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[94]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[95]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[96]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[97]  C. Ibbs,et al.  The effects of membership function on fuzzy reasoning , 1991 .

[98]  Abraham Kandel,et al.  Compensatory neurofuzzy systems with fast learning algorithms , 1998, IEEE Trans. Neural Networks.

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

[100]  Xin Yao,et al.  A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..

[101]  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.

[102]  Hitoshi Iba,et al.  Learning polynomial feedforward neural networks by genetic programming and backpropagation , 2003, IEEE Trans. Neural Networks.

[103]  Tariq Samad,et al.  Designing Application-Specific Neural Networks Using the Genetic Algorithm , 1989, NIPS.

[104]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[105]  M. Koppen,et al.  A neural network that uses evolutionary learning , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[106]  Jaehong Park,et al.  Evolutionary projection neural networks , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[107]  Joel W. Burdick,et al.  Global descent replaces gradient descent to avoid local minima problem in learning with artificial neural networks , 1993, IEEE International Conference on Neural Networks.

[108]  X. Yao Evolving Artificial Neural Networks , 1999 .

[109]  David J. Chalmers,et al.  The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .

[110]  Tatsuo Higuchi,et al.  Evolutionary learning of nearest-neighbor MLP , 1996, IEEE Trans. Neural Networks.

[111]  Scott D. Sudhoff,et al.  Estimating Regions of Asymptotic Stability of Power Electronics Systems Using Genetic Algorithms , 2010, IEEE Transactions on Control Systems Technology.

[112]  Kay Chen Tan,et al.  Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition , 2006, IEEE Transactions on Neural Networks.

[113]  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.

[114]  G. Schnitger,et al.  Efficient Approximation with Neural Networks: A Comparison of Gate Functions , 2005 .

[115]  Brijesh Verma,et al.  A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms , 2007, Appl. Soft Comput..

[116]  Akihiko Konagaya,et al.  A Fine-Grained Parallel Genetic Algorithm for Distributed Parallel Systems , 1993, ICGA.

[117]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[118]  Francisco Luna,et al.  Advances in parallel heterogeneous genetic algorithms for continuous optimization , 2004 .

[119]  Tariq Samad,et al.  Towards the Genetic Synthesisof Neural Networks , 1989, ICGA.

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

[121]  Marco Saerens,et al.  A neural controller , 1989 .

[122]  Wei Gao Study on new evolutionary neural network , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[123]  Zbigniew Skolicki,et al.  The influence of migration sizes and intervals on island models , 2005, GECCO '05.

[124]  Hisao Ishibuchi,et al.  Neural networks that learn from fuzzy if-then rules , 1993, IEEE Trans. Fuzzy Syst..

[125]  Jagannathan Sarangapani,et al.  Neural Network Control of Nonlinear Discrete-Time Systems , 2018 .

[126]  William B. Langdon,et al.  Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! , 1998 .

[127]  David R. Jefferson,et al.  Selection in Massively Parallel Genetic Algorithms , 1991, ICGA.

[128]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[129]  O. De Jesus,et al.  Smoothing the control action for NARMA-L2 controllers , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[130]  Yoshio Hirose,et al.  Backpropagation algorithm which varies the number of hidden units , 1989, International 1989 Joint Conference on Neural Networks.

[131]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[132]  Shun'ichi Tano,et al.  Deep combination of fuzzy inference and neural network in fuzzy inference software - FINEST , 1996, Fuzzy Sets Syst..

[133]  Kumpati S. Narendra,et al.  Adaptive control: neural network applications , 1998 .

[134]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[135]  George G. Robertson,et al.  Parallel Implementation of Genetic Algorithms in a Classifier Rystem , 1987, ICGA.

[136]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[137]  Vasant Honavar,et al.  Generative learning structures for generalized connectionist networks , 1990 .

[138]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[139]  Ali Mohebbi,et al.  Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers. , 2008, Journal of hazardous materials.

[140]  Robert F. Port,et al.  Fractally configured neural networks , 1991, Neural Networks.

[141]  Jihoon Yang,et al.  Constructive Neural-Network Learning Algorithms for Pattern Classification , 2000 .

[142]  David E. Goldberg,et al.  Efficient Parallel Genetic Algorithms: Theory and Practice , 2000 .

[143]  Kristin P. Bennett,et al.  Feature selection for in-silico drug design using genetic algorithms and neural networks , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[144]  P. J. Haley,et al.  Neural generalized predictive control , 1996, Proceedings of the 1996 IEEE International Symposium on Intelligent Control.

[145]  Derek A. Linkens,et al.  A hybrid neuro-fuzzy PID controller , 1998, Fuzzy Sets Syst..

[146]  Peter J. Fleming,et al.  Parallel Genetic Algorithms: A Survey , 1994 .

[147]  Patrick Siarry,et al.  A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases , 1998, Fuzzy Sets Syst..

[148]  Shun'ichi Tano,et al.  Definition and formulation of backward-reasoning with fuzzy if... then... rules , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[149]  Takayuki Yamada,et al.  Neural network controller using autotuning method for nonlinear functions , 1992, IEEE Trans. Neural Networks.

[150]  Eduardo D. Sontag,et al.  Neural Networks for Control , 1993 .

[151]  Shun'ichi Tano,et al.  Definition and tuning of unit-based fuzzy systems in FINEST , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[152]  Hideyuki Takagi Application of neural networks and fuzzy logic to consumer products , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[153]  Christian Igel,et al.  Effects of phenotypic redundancy in structure optimization , 2002, IEEE Trans. Evol. Comput..

[154]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[155]  R. E. Uhrig,et al.  Using genetic algorithms to select inputs for neural networks , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[156]  Rudolf Kruse,et al.  Neuro-fuzzy systems for function approximation , 1999, Fuzzy Sets Syst..

[157]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..