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ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 5. Vol. 24. 2018
A. S. Akopov, D. Sc. Professor, e-mail: aakopov@hse.ru, National Research University Higher School of Economics, Moscow, Central Economics and Mathematics Institute RAS, Moscow, A. L. Beklaryan, Ph. D., Associate Professor, e-mail: abeklaryan@hse.ru, National Research University Higher School of Economics, Moscow, N. K. Khachatryan, Ph. D., Associate Professor, e-mail: nerses@cemi.rssi.ru, nkhachatryan@hse.ru, National Research University Higher School of Economics, Moscow, 101000, Central Economics and Mathematics Institute RAS, Moscow, 117418, A. V. Fomin, Ph. D., Senior Lecturer, e-mail: afomin@hse.ru, National Research University Higher School of Economics, Moscow, 101000, Russian Federation
Development of an Adaptive Genetic Optimization Algorithm using Agent Modeling Methods
This article presents a new approach to developing an adaptive genetic optimization algorithm (MAGAMO/A) using agent modeling techniques. The peculiarity of this approach is the support of the mechanism of adaptive control of key characteristics of GA, in particular, the values of the probabilities of crossover operators and mutations, their types and other important characteristics that affect the population diversity and the rate of convergence of GA. Support for adaptive control is provided by using the mechanism of agent state charts and the specified rules of transition between the corresponding states that determine the values of the control parameters of the GA at the individual level of each agent-process. The review of the most popular GAs used for multicriteria optimization, including SPEA2, NSGA, MOEA, etc., is reviewed. The main metrics for evaluating the effectiveness of such GAs (Hypervolume, Generational Distance, distance between solutions on the Pareto boundary, etc.) are considered. The efficiency of the developed approach in the solution of optimization problems of large dimension on several test examples and in comparison with other known GA is demonstrated. The main directions of further research in the field of development of agent-oriented genetic algorithms are formulated.
Keywords: genetic algorithm, Pareto front, evolutionary computation, accentual modeling, multi-objective optimization
P. 321-329