Abstract
Function Optimization is a typical problem. A mixed crossover strategy genetic algorithm for function optimization is proposed in this paper. Four crossover strategies are mixed in this algorithm and the performance is improved compared with traditional genetic algorithm using single crossover strategy. The numerical experiment is carried out on nine traditional functions and the results show that the proposed algorithm is superior to four single pure crossover strategy genetic algorithms in the convergence rate for function optimization problems.
* This work is supported by the Nature Science Foundation of Inner Mongolian in P.R.China (200711020807), by the Scientific Research Project of Inner Mongolia University for Nationalities (MDB2007132, YB0706, MDK2007032).
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Zhuang, Ly., Dong, Hb., Jiang, Jq., Song, Cy. (2008). A Genetic Algorithm Using a Mixed Crossover Strategy. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_94
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DOI: https://doi.org/10.1007/978-3-540-87732-5_94
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