O. Ye. Mochalin


An optimization problem is formally formulated. The main advantages and disadvantages of classical optimization methods are considered
for this problem. Basic prerequisites for the emergence and history development of the instrument of genetic algorithms are highlighted. The current state of the bibliography which is dedicated to the use of search genetic algorithms is analyzed. The basic ideas and underlying principles of genetic algorithms functioning are considered. A review of the most commonly used genetic operators: crossover and mutation, is made. The basic steps of classical genetic algorithm operation are analyzed in detail. The coding problem of solutions in the chromosomes and the selection of individual pairs for crossbreeding are considered. Some common selection strategies are presented as well. The basic benefits of the binary coding of solutions in the chromosomes that using Gray code are formulated. The recommendations are also given on the using of solutions real coding in different situations. The two main classes of parallel genetic algorithms: «islands» and «master – slave», are described. The example of using a genetic algorithm to optimize multiextremal function depending on a large number of parameters is showed. Experimental data are presented which confirm the benefits of graphic processors using in parallel implementation of genetic algorithm. The expediency recommendations of the use of genetic algorithms in different situations are set out.


optimization, genetic algorithm, Soft Computing, genetic operators, coding of solutions.


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