Research On Genetic Algorithm Based On Improved BA Network

In the 1970 s,John H.Holland,a professor of the University of Michigan was inspired by Darwin's theory of biological evolution and genetics to establish the basic framework of Genetic Algorithm(GA).As soon as GA was proposed,it has attracted close attention from scholars and has been applied in many fields.The basic theory of GA was gradually improved.When GAs used to solving complex problems,there is a phenomenon of "premature convergence",which leads to a decrease in algorithm performance.Therefore,how to improve the performance of GAs is a research hotspot in this field.Complex Network is an emerging discipline in the 1990 s.The main idea of complex network is to use network characteristics to describe phenomena such as physics,biology,and society.Establish mathematical models of these phenomena to predict and analyze network behavior,and use the static and dynamic characteristics of the network to explain these phenomena.The rise of complex networks provides a new view for more effective control of heuristic algorithms.In this paper,the improvement of genetic algorithm is studied.Genetic Algorithm is studied as a network interaction system,and analyzed with complex network ideas.Nodes represent population individuals and connections represent information transmission paths between individuals.A genetic algorithm based on an improved BA network is proposed.The improved GA realizes further improvement of the network structure and improves the selection strategy of the traditional GA and the population size adaptive strategy adopted in response to the incremental use of nodes in the network.The specific steps and methods of the improved algorithm are given in detail and the performance of the improved algorithm is verified by numerical experiments.The results show that the improved algorithm performs better both than the basic GA and GA based on ordinary BA network.The results have a certain guiding role for the improvement of GA.Constrained evolutionary algorithm is an important research topic in the field of computational evolution.How to efficiently deal with constraints is a key problem.In this paper,by improving the basic penalty function method,an improved algorithm combined with GA based on an improved BA network is proposed to improve the original penalty function strategy and enhance the processing capacity of the constraint.Numerical experiments verify the improved algorithm for constrained optimization.The processing performance of the problem,the research results have a certain significance for the processing of constrained optimization problems.