Abstract
Many adaptive schemes for controlling the probabilities of crossover and mutation in genetic algorithms with fuzzy logic have been reported in recent years. However, there has not been known work on comparative studies of these algorithms. In this paper, several fuzzy genetic algorithms are briefly summarized first, and they are studied in comparison with each other under the same simulation conditions. The simulation results are analyzed in terms of search speed and search quality.
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Li, Q., Yin, Y., Wang, Z., Liu, G. (2007). Comparative Studies of Fuzzy Genetic Algorithms. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_31
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DOI: https://doi.org/10.1007/978-3-540-72393-6_31
Publisher Name: Springer, Berlin, Heidelberg
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