Abstract
A new Self\|adaptive hybrid evolutionary algorithm (SHEA) is presented in this paper based on other researchers' work by adding some ideas of Monte Carlo Simulation of gradient, immune operator and simulated annealing to Evolutionary algorithms, which is to avoid Simple Evolutionary algorithms' low evolution speed and premature. The new algorithm uses different evolution operators self\|adaptively during different evolution periods and it has high global searching performance in forepart of evolution and high local performance in late evolution, and it can converge to global optimum quickly. As the simulated experimental results show, Self\|adaptive hybrid evolutionary algorithm has the advantages of high precision and robustness and fast convergence.
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