Clustering analysis based on Chaos Genetic Algorithm

To improve the accuracy of clustering classification, the Chaos Genetic Algorithm was proposed. In this algorithm, the ergodic property of chaos phenomenon is used to optimize the initial population, so it can accelerate the convergence of Genetic Algorithms. Chaotic systems are sensitive to initial condition system parameters. In order to escape from local optimums, the chaos operator was applied to optimize the individuals after the process of selection operator, crossover operator and mutation operator. Theory and experiment shows that the algorithm can get global optimum clustering center, and greatly improve the amplitude of operation.