Application of collaborative filtering methods in the problem of predicting the performance of population optimization algorithms


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Abstract

In this paper we propose an approach to solving the problem of choosing the most efficient algorithm for solving a given continuous optimization problem, based on the using of collaborative filtering methods. A prototype of a software system based on a set of the most popular population optimization algorithms and a system of test objective functions for continuous optimization problems is described. The implementation of several methods for predicting the performance of a given algorithm is considered. The results of computational experiments and comparison of the considered methods are presented.

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About the authors

Nikolay M. Ershov

Lomonosov Moscow State University (MSU)

Email: ershov@gse.cs.msu.ru
Cand. Sci. (Phys.-Math.); senior research associate at the Faculty of Computational Mathematics and Cybernetics Moscow, Russian Federation

Olga P. Nikitina

Lomonosov Moscow State University (MSU)

Email: nikitinaolga_msu@mail.ru
Faculty of Computational Mathematics and Cybernetics Moscow, Russian Federation

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