In this paper, the concept of a multidimensional discrete spectral measure is introduced in the context of its application to the real-valued evolutionary algorithms. The notion of a discrete spectral measure makes it possible to uniquely define a class of multivariate heavy-tailed distributions, that have recently received substantial attention of the evolutionary optimization community. In particular, an adaptation procedure known from the distribution estimation algorithms (EDAs) is considered and the resulting estimated distribution is compared with the optimally selected referential distribution.
In this paper the concept of two-dimensional discrete spectral measure is introduced in the context of its application to real-valued evolutionary strategy (1, lambda)ES. The notion of discrete spectral measure makes it possible to uniquely define a class of multivariate heavy-tailed distributions, that have received more and more attention of evolutionary optimization community, recently. In particular, an adaptation procedure known from the class of estimation of distribution algorithms (EDAs) is proposed. The effectiveness of the evolutionary strategy is tested by means of a set popular benchmark functions.
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