Abstract
A general optimization algorithm, which in some areas successfully competes with simulated annealing and the Kernighan-Lin algorithm, as well as special heuristics, is presented. It gains speed by taking advantage of the structure of the objective function in order to reduce the search space. Results obtained from the implementation of the algorithm on three problems are presented. >
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