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Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce

Fuzzy logic --- Genetic Algorithms --- Fuzzy logic. --- Genetic algorithms. --- Genetic algorithms --- E-books --- Nonlinear logic --- Fuzzy mathematics --- Logic, Symbolic and mathematical --- Fuzzy systems --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Logique floue --- Systèmes flous

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Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

genetische algoritmen --- Artificial intelligence. Robotics. Simulation. Graphics --- robots --- Genetics --- Analysis of algorithms and problem complexity--See also {681.3*B6} {681.3*B7} {681.3*F13} --- Learning: analogies concept learning induction knowledge acquisition language acquisition parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*I26 Learning: analogies concept learning induction knowledge acquisition language acquisition parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*F2 Analysis of algorithms and problem complexity--See also {681.3*B6} {681.3*B7} {681.3*F13} --- Genetische algoritmen --- Genetische algoritmen. --- 681.3*F2 --- 681.3*I26 --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*F2 Analysis of algorithms and problem complexity--See also {681.3*B6}; {681.3*B7}; {681.3*F13} --- Analysis of algorithms and problem complexity--See also {681.3*B6}; {681.3*B7}; {681.3*F13} --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Biomathematics --- Computer simulation --- Mathematical models --- #TELE:SISTA --- Computer science --- Computer simulation. --- Mathematical models. --- Genetic algorithms. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- COMPUTER SCIENCE/General