Abstract
Genetic algorithm is an effective global optimization method and it has been widely used in many fields. In order to solve some problems of standard genetic algorithms, such as slow convergence and easy to fall into local extremum, an improved adaptive genetic algorithm is presented. Specifically, in crossover and mutation operations, the algorithm considers both the influence of the generation number of the evolution and the effect of the different adaptive individuals in the contemporary population. In addition, a new parameter (i.e., the generation number by which the same optimal solution is maintained) is introduced to adjust the crossover and mutation probabilities, so as to improve the exploration ability of the algorithm. With the above two adjustment strategies, the performance of the adaptive algorithm can be greatly improved. The experimental results of four functions showed that the proposed algorithm has a good improvement in aspects of convergent ability and the ability to jump out of local extremum.
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Acknowledgements
This research is supported by the National Science Foundation of China (No. 31300938 and No. 61462053), and the Natural Science Foundation of Yunnan Province of China (No. 2016FB107).
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Zhao, Y., Qian, Q., Wang, F. (2019). An Adaptive Genetic Algorithm with Dual Adjustment Strategies and Its Performance Analysis. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-00214-5_154
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DOI: https://doi.org/10.1007/978-3-030-00214-5_154
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