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This is a comprehensive overview of the basics of fuzzy control, which also brings together some recent research results in soft computing, in particular fuzzy logic using genetic algorithms and neural networks. This book offers researchers not only a solid background but also a snapshot of the current state of the art in this field.

Genetic algorithms. --- Mathematical optimization. --- Epistasis (Genetics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Genetic epistasis --- Gene expression --- Genetic regulation --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Genetics --- Computer science. --- Computational complexity. --- Combinatorics. --- Optimization. --- Genetics and Population Dynamics. --- Mathematics of Computing. --- Discrete Mathematics in Computer Science. --- Mathematics. --- Combinatorics --- Algebra --- Complexity, Computational --- Electronic data processing --- Machine theory --- Informatics --- Science --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Biomathematics. --- Computer science—Mathematics. --- Mathematics

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The success of a genetic algorithm when applied to an optimization problem depends upon several features present or absent in the problem to be solved, including the quality of the encoding of data, the geometric structure of the search space, deception or epistasis. This book deals essentially with the latter notion, presenting for the first time a complete state-of-the-art research on this notion, in a structured completely self-contained and methodical way. In particular, it contains a refresher on the linear algebra used in the text as well as an elementary introductory chapter on genetic

Genetic algorithms. --- Mathematical optimization. --- Epistasis (Genetics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Genetic epistasis --- Gene expression --- Genetic regulation --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Computer science. --- Computational complexity. --- Genetics --- Combinatorics. --- Mathematics of Computing. --- Discrete Mathematics in Computer Science. --- Genetics and Population Dynamics. --- Optimization. --- Mathematics. --- Combinatorics --- Algebra --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Complexity, Computational --- Electronic data processing --- Machine theory --- Informatics --- Science --- Computer science—Mathematics. --- Biomathematics. --- Mathematics --- Computer science --- Discrete mathematics. --- Population genetics. --- Population Genetics. --- Discrete Mathematics. --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Computer mathematics