Linkage Information Processing In Distribution Estimation Algorithms

The last few years there has been an increasing amount of interest in the field of distribution estimation optimization algorithms. As more techniques are introduced, the variety in tested distribution structures increases. In this paper we analyze the implications of the form of such a structure. We show that learning the linkage relations alone and using them directly in a distribution estimation algorithm to generate new samples is not sufficient for building competent evolutionary algorithms. The information needs to be processed to identify and use the building blocks.

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