A guided evolutionary computation technique as function optimizer

In this paper, we present a regionally guided approach to function optimization. The proposed technique is called "Guided Evolutionary Simulated Annealing". It combines the simulated annealing and simulated evolution in a novel way. The technique has a mechanism that the search will focus on more "promising" areas. The solution is evolved under regional guidance. The characteristics of the proposed technique are given. We illustrate the technique with two examples. The results of both examples indicate that the GESA technique yields optimal or near-optimal solutions, superior to a version of simulated evolution and a version of parallel simulated annealing.<
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