A new mutation operator and its application

A real-coded GA based on an annealing chaotic mutation operator is proposed. By introducing the intrinsic stochastic property and ergodicity of chaos movement and variable evolutionary rate, this algorithm can better simulate the process of biologic evolution and possess better hill-climbing ability. It adaptively changes the operating order of evolutionary operators in the different evolutionary stages. So it overcomes the shortcoming of premature convergence and stagnation, and effectively solves the problem of global convergence. Compared with some self-adaptive GAs, the test results show that this algorithm is easy to implement and its efficiency is higher in the rate of convergence, accuracy and reliability, so it is effective for optimization problems