A multiple population Boltzmann machine
A. Schultz · 2002
Boltzmann machines and genetic algorithms have been successfully applied to function optimization problems. The model developed, merges these approaches to obtain a system that has the best features of both. The composite system offers capabilities difficult to obtain with standard genetic algorithms. It yields automatic niche formation and at the same time it avoids premature convergence. It does not have the Boltzmann machines problem of getting trapped in a local maxima. The model has a temperature parameter that can be used to obtain convergence to a global optimum as is done for simulated annealing. The single population Boltzmann machine is extended to a multiple population and an associated set of genetic operators. It is shown that the equilibrium probability distribution is Gibb's. Computer simulations that show niche formation are presented.>