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Comprehensive, up to date handbook, covering one of the hottest topics in research and application-oriented science and engineering
Includes a synergy of various approaches and new qualities to be gained by using hybrid approaches
A timely and up-to-date reference, edited by internationally renowned experts
This is the first book covering the basics and the state of the art and important applications of the complete growing discipline of computational intelligence. This comprehensive handbook presents a unique synergy of various approaches and new qualities to be gained by using hybrid approaches, incl. inspirations from biology and living organisms and animate systems. The text is organized in 7 main parts foundations, fuzzy sets, rough sets, evolutionary computation, neural networks, swarm intelligence and hybrid computational intelligence systems.
Part A Foundations Many-Valued and Fuzzy Logics.- Possibility Theory and its Applications: Where Do we Stand?.- Aggregation Functions on [0; 1].- Monotone Measures-Based Integrals.- The Origin of Fuzzy Extensions.- From Type-2 Fuzzy Sets to Atanassov’s Intuitionistic Fuzzy Sets.- F-Transform.- Fuzzy Linear Programming and Duality.- Basic Solutions of Fuzzy Coalitional Games Part B Fuzzy Logic Basics of Fuzzy Sets.- Fuzzy Relations: Past, Present and Future.- Fuzzy Implications.- Fuzzy Rule Based Systems.- Interpretability of Fuzzy Systems.- Fuzzy Clustering.- An Algebraic Model of Reasoning to Support Zadeh’s CwW.- Fuzzy Control.- Interval Type-2 Fuzzy PID Controllers.- Soft Computing in Database and Information Management.- Application of Fuzzy Techniques to Autonomous Robots Part C Rough Sets Foundations of Rough Sets.- Rough Set Methodology for Decision Aiding.- Rule Induction from Rough Approximations.- Probabilistic Rough Sets.- Generalized Rough Sets.- Fuzzy-Rough Hybridization Part D Neural Networks Artificial Neural Network Models .- Deep and Modular Neural Networks.- Machine Learning.- Theoretical Methods in Machine Learning.- Probabilistic Modeling in Machine Learning.- Kernel Methods.- Neurodynamics .- Computational Neuroscience - Biophysical Modeling of Neural Systems .- Computational Models of Cognitive and Motor Control.- Cognitive Architectures and Agents.- Embodied Intelligence.- Neuromorphic Engineering.- Neuroengineering -- Sensorimotor-Computer Interfaces.- Evolving Connectionist Systems .- Machine Learning Applications Part E Evolutionary Computation Genetic Algorithms.- Genetic Programming.- Evolution Strategies.- Distribution Algorithms.- Parallel Evolutionary Algorithms.- Learning Classifier Systems.- Indicator-Based Selection.- Multiobjective Evolutionary Algorithms.- Parallel Multiobjective Evolutionary Algorithms.- Many-objective Problems: Challenges and Methods.- Memetic and Hybrid Evolutionary Algorithms.- Design of Representations and Search Operators.- Stochastic Local Search Algorithms.- Parallel Evolutionary Combinatorial Optimization.- How to Create Generalizable Results.- Computational Intelligence in Industrial Applications.- Solving Phase Equilibrium Problems.- Optimization of Machining Problems.- Physics-Based Surrogate Modelling in Evolutionary Optimization for Aerodynamic Design.- Evolutionary Combinatorial Optimization for Knowledge Discovery in Bioinformatics .- Integration of Metaheuristics and Constraint Programming.- Assessing the Effects of Recombination with the Graph Coloring Problem.- Metaheuristic Algorithms and Tree Decomposition.- Evolutionary Computation and Constraint Satisfaction Part F Swarm Intelligence Swarm Intelligence in Optimization and Robotics.- Preference-Based Multiobjective Particle Swarm Optimization.- Ant Colony Optimization for the Minimum-Weight Rooted Arborescence Problem.- Intelligent Swarm of Markovian Agents.- Honey Bee Social Foraging Algorithm.- Fundamental Collective Behaviors in Swarm Robotics.- Collective Manipulation and Construction.- Reconfigurable Robots.- Probabilistic Modeling of Swarming Systems Part G Hybrid Systems Robust Evolving Cloud-Based Controller.- Evolving Embedded Fuzzy Controllers.- Multiobjective Genetic Fuzzy Systems.- Bio-Inspired Optimization.- Pattern Recognition with Modular Neural Networks.- Optimization of Interval Fuzzy Controllers for Autonomous Mobile Robots.- Implementation of Bio-Inspired Optimization Methods on Graphic Processing Units
Acknowledgements.- About the Authors.- Subject Index