Abstract
ABSTRACT An annealing evolution algorithm is applied to the optimization of continuous problems. The algorithm uses an evolutionary strategy to guide the search in simulated annealing. Furthermore, we discuss an implementation of the algorithm and compare its performance with the conventional simulated annealing algorithm and the parallel genetic algorithm. The performance evaluation is carried out for a standard set of test functions from the literatures. Here a breakthrough can be reported. The annealing evolution algorithm is able to find the global minimum of Rastrigin's function of dimension 500 on the VAX8600 machine.
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