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Techniques exist to synthesize software architecture using genetic algorithms that employ transformations based on mutations and crossover. In this paper, we demonstrate that complementary crossover can significantly improve this technique. We study two versions of complementary crossover, one in which parents are selected so that they complement each other but the genes are inherited randomly from...
Urban planning is a important part of urban construction, spatial distribution is subordinate to urban planning, it is difficult to obtain a satisfactory solution by traditional optimal methods. In view of the factors that influence urban spatial distribution are diverse and dynamic and city needs to keep the balance continuously in the development, genetic algorithm method is selected because it...
The work is devoted to definition of genetic algorithm significant characteristics, effective combinations and constructions of genetic operators, optimum likelihood parameters. The analysis of genetic operators such as tournament, simple and proportional selection, and also a crossover (casual, multi-point, universal) is lead in the work. The experiments are conducted and recommendations on application...
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