In this article the analysis of materials on genetic algorithms is carried out. The main
ideas and principles underlying work of genetic algorithms are considered. The basic stages
of the classical genetic algorithm work are analyzed in detail. The review of the most common
methods of selection (roulette and tournament), types of crossover (single-point and uniform)
and strategies of formation of generations (classical and elitist) is executed.
On test functions the research of genetic algorithm with different methods of selection,
types of crossover and strategy of formation of generations is carried out. For each type of
algorithm, an estimate of the probability of finding a true solution is given. The received
results of the experiments are carefully analyzed. The advantages and disadvantages of
different methods of selection, types of crossover, strategy of formation of generations are
revealed. The recommendations on the expediency of using genetic algorithms in various
situations are stated. The possible directions for further research are defined.
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Maraev Vyacheslav Sergeevich
Siberian Federal University, The Institute of Space and Information Technology
Krasnoyarsk, Russian Federation
Bezzubenko E. A.
Siberian Federal University, The Institute of Space and Information Technology
Krasnoyarsk, Russian Federation
Cherkashin D. A.
Siberian Federal University, The Institute of Space and Information Technology
Krasnoyarsk, Russian Federation
Mikhalev Anton Sergeevich
Email: asmikhalev@sfu-kras.ru
Siberian Federal University, The Institute of Space and Information Technology
Krasnoyarsk, Russian Federation
