Abstract
Genetic algorithm is the simulation of the natural biological evolution principle, through the selection, crossover, mutation three genetic operators to complete the evolution and inheritance of nature. The advantage of genetic algorithm is its strong ability of global optimization. However, genetic algorithm has premature phenomenon, which makes the algorithm produce sub optimal solution prematurely. In this paper, adaptive strategy is introduced. In the process of algorithm implementation, crossover probability and mutation probability are adjusted dynamically, and population evolution speed is automatically adjusted to ensure that the algorithm finally obtains the global optimal solution.
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Zhang, L., Shu, Y. (2021). Research on K-Means Clustering Algorithm Based on Improved Genetic Algorithm. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_46
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DOI: https://doi.org/10.1007/978-981-16-3150-4_46
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