Research And Application Of Improved Adaptive Genetic Algorithm

Artificial intelligence has been developed rapidly in recent years.Computational intelligence is an important research direction in the field of artificial intelligence.How to use intelligent computing to solve complex practical problems has become one of the requirements for improving the economic development and the core competitiveness of the country.Genetic algorithm is one of the popular algorithms in the field of computational intelligence.It has been widely used in image retrieval,path planning,artificial psychology and other fields related to production and living conditions.Genetic algorithm is an intelligent algorithm with efficient random search and optimization ability.Unlike other traditional algorithms,genetic algorithms mimic the natural selection mechanism of biological evolution and can solve complex problems.The genetic algorithm generates an initial population by coding,and then evaluates the the population by fitness function.According to the fitness values,genetic operations such as selection,crossover,and mutation are performed.In addition,because of the limitations of standard genetic algorithms,an adaptive inheritance algorithm by dynamic changing of crossover and mutation probabilities has been proposed and widely used.This paper illustrates the role of basic and adaptive genetic algorithms in the field of optimization through a detailed introduction to the basic theory of genetic algorithms.After that,a new construction method of the crossover and mutation operator is given.The analysis of the experimental data proves the efficiency of the proposed algorithm over the other classical adaptive genetic algorithms.Finally,the proposed adaptive genetic operator is applied to the optimization of the pipe laying in the heating system.The experimental results show that the new operators are better than other methods in the optimization of the pipe laying problems.