Chainlike Multiagents Genetic Algorithm Based On Dynamic Competed Strategy

Genetic algorithm is a global optimization algorithm enlightened biological evolution theory. Because of its simplicity, robustness and no need for lots of prior knoeledge, genetic algorithm is applied in many fields. However, currently the algorithm has two severe disadvantages needing to be improved.In order to improve the two disadvantages, one chain-like agent genetic algorithm (CAGA) is proposed in this paper. The CAGA adopts real coding, introduces agent standing for solution and living in chain-like environment, searchs global optima through the cooperation and competition of agents. The algorithm adopts dynamic neighborhood competition operator, neithborhood orthogonal crossover operator and neighborhood adaptive mutation operator to improve searching efficiency of agents, thereby improving optimization performance of the algorithm. Lots of benchmark test functions are used for comparing the optimization performance of CAGA and other representative GAs in the experiments. The experimental results show that CAGA can obtain higher optimization precision and can converge to the domain close to global optima rapidly.The CAGA is an agent genetic algorithm with single population, it can not realize co-evolution with multi-population, so the optimization speed can not be improved greatly. Based on the reason, further research is done and Multi-population agents genetic algorithm (MPAGA) is proposed in this paper. This algorithm adopts parallel searching mode combining co-eovlution idea. The major process is: firstly the whole population is divided into many sub-populations. Then each sub-popualtion evolves as CAGA does. Sub-populations co-evolve and search global optima through the shared agents, thereby improving optimization speed. Lots of benchmark test functions are used for comparing the optimization performance of MPAGA and one popular agent genetic algorithm in the experiments. The experimental results show that MPAGA not only can improve optimization speed greatly, but also obtain satisfied optimization precision.Besides, this paper briefly discusses the application of CAGA into feature selection problems. In order to use the algorithm in feature selection problems, the binary coding is used as coding strategy, distance measurement is used for evaluation criterion and BP neural network is used as classifier for classification test of feature subsets. In the experiments, several datasets is used for showing the performance of CAGA. The experimental results show that the CAGA can obtain less features, and higher classification accuracy compared with other popular GAs.