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Quasi Descent Algorithm for Function Optimization Based on Genetic Algorithm

Liu Xueying Shi Baochang  
By adding a quasi descent step into the traditional optimization, the convergence of the algorithm is improved. The step is solved by the genetic algorithm, so that it not only enhances the global search ability of the traditional algorithm but also retains its convergence. The numerical computation shows that this method possesses the advantages of both the genetic algorithm and the traditional one. It is a feasible hybrid algorithm for function optimization.
【Fund】: 国家自然科学基金
【CateGory Index】: O241.5
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