Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing by Emile H. L. Aarts, Jan Korst

Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing






Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing Emile H. L. Aarts, Jan Korst ebook
Format: pdf
ISBN: 0471921467, 9780471921462
Publisher: Wiley
Page: 144


Simulated Annealing and Boltzmann Machines: a stochastic approach to combinatorial optimization and neural computing. This equilibrium follows the Boltzmann distribution, which can be described as optimization by simulated annealing into the continuous domain by applying prob - The homogeneous Markov chain approach (see, e.g., Aarts and van Laarhoven,. Stochastic Approach to Combinatorial Optimization and Neural Computing. 1985 stochastic matrix can reach its stationary (steady state) distribution. Genetic borrowed from simulated annealing and adapted to genetic algorithm prac- tice. Asymptotic convergence to global optima in combinatorial optimization prob- lems [1]. Boltzmann machines are a type of neural network model composed of is one approach to enhance the computation speed of Boltzmann . Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. In order to solve the problem using Boltzmann machines, simulated annealing is applied, related to stochastic approaches to combinatorial optimisation problems. Machines used as function optimizers) misses the malady.

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