On promising regions and optimization effectiveness of continuous and deceptive functions

This paper evaluates the performance of three evolutionary algorithms to globally optimize complex continuous functions. The performance is evaluated by measuring the algorithms success rate to find the global optimum in several trials. At each set of trials, the search-space is reduced to be closer to the global optimum, so that the starting population is generated in an even more promising region. According to the results, it is possible to can conclude that, in high complexity problems, a good performance of classical evolutionary algorithms can not be expected. The paper also evaluates the performance of an evolutionary algorithm in a deceptive function. In this case, the reduced search-space is the model which generates the deceptive function. The success rates with and without the use of the starting model were compared. In this case, the use of a better starting model substantially increases the performance.

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