Computational Intelligence for Optimization
The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.
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HEURISTIC SEARCH METHODS
HOPFIELD NEURAL NETWORKS
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10-city problem annealing procedure assigned binary strings Boltzmann machine Chapter chromosomes cities combinatorial optimization computational configuration connection matrix constraints convergence cooling schedule corresponding cost function critical temperature crossover operator crossover sites data throughput decrement defined denotes eigenvalue EXPLORATORY PROBLEMS fitness value gene genetic algorithm genetic operators goal node hardlimiter heuristic heuristic search Hopfield energy function Hopfield network initial input iterations Lagrange parameters Lyapunov function Markov chain mean field annealing mean field equation mean field variable minimized minimum cost minimum spanning tree model point multiprocessor mutation operator neural networks neurons nonlinearity number of strings obtained optimal schedule permutations point pattern polynomial probability processors random number randomly satellite SBS problem scheduleable subset schema search nodes selected shown in Figure sigmoid sigmoid function simulated annealing slots solving the TSP stochastic machines strings in P(t tanh task graph term tion transition traveling salesman problem updating valid solution vector
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Adaptive Image Processing: A Computational Intelligence Perspective
Ling Guan,Stuart William Perry,Hau San Wong
No preview available - 2001