Chaos-Genetic Algorithm Based on the Cat Map and Its Application on Seismic Wavelet Estimation

This paper proposes the chaos-genetic algorithm (CGA) based on the cat map in order to optimize a multidimensional and multimodal non-linear cost function for the seismic wavelet. The algorithm uses the initial sensitivity of the cat map to expand the scope of the search, and uses the ergodicity of the cat map to search the chaotic variables. Thus, reduces the data redundancy, maintains the diversity of population, and solves the problem of local optimum effectively. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.