Abstract
Clustering is an unsupervised learning task that groups objects in a multi-dimensional space based on similarity criteria. The goal is to make groups that contain objects that are similar to each other and different from other groups. This work proposes a novelty genetic algorithm to solve the clustering problem based on partitions and estimate automatically the number of clusters. The proposal, GASGO (Genetic Algorithm with Specialized Genetic Operators), includes a representation based on codebooks and the use of specialized and improvement mutation and crossover operators that achieve a high performance to solve clustering problems. The experimental study evaluates 10 clustering validation indexes, 46 data sets, and 8 previous proposals of GAs for clustering considered the state of the art in the area. Results show that GASGO improves the performance for all CVIs compared to the previous proposals.
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Acknowledgements
This research was supported in part by grant PID2020-115832GB-I00 funded by MICIN/AEI/10.13039/501100011033 and by the ProyExcel-0069 project of University, Research and Innovation Department of the Andalusian Board and the European Regional Development Fund.
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Robles-Berumen, H., Zafra, A., Ventura, S. (2023). A Novel Genetic Algorithm with Specialized Genetic Operators for Clustering. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_39
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DOI: https://doi.org/10.1007/978-3-031-40725-3_39
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