Anatoly Sukov


This paper examines the algorithm of differential evolution that has appeared rather recently. This algorithm ascribed by its developers to a class of evolutionary algorithms is a comparatively non-complicated technique o f solution search as applied to multiparameter optimisation tasks. Nevertheless, there are two essential factors preventing from wide application of the considered solution search technique. One of them lies in the principle of coding vectors (variables) that constitute a population the algorithm works with. The second problem is of pure technical character: in the process of search, stagnation occurs, or impossibility to find new solutions, when there is no optimal solution in the population and the vectors available are not heterogeneous. Besides studying search possibilities (limitations) of the differential evolution, some ways to cope with the problem of stagnation so-as to raise the performance of the algorithm are also suggested.

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Back T. and Schwefel H.-P. (1995). Evolution Strategies I: Variants and their computational implementation. Genetic Algorithms in Engineering and Computer Science, editors Periaux J. and Winter G. John Wiley & Sons Ltd.

Bentley P.J. (1999). An Introduction to Evolutionary Design by Computers. In: Evolutionary Design by Computers (Bentley P. J.,Ed.) Morgan Kaufmann, p. 1-73.

Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison-Wesley.

Lampinen J. and Zelinka I. (2000). On Stagnation of the Differential Evolution Algorithm. Proceedings MENDEL 2000, 6th International Conference on Soft Computing. - Brno, June 7-9, 2000, p. 76-83.

DOI: https://doi.org/10.17770/etr2001vol1.1956


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