Abstract
Our previous PSO-PARSIMONY methodology (a heuristic to search for accurate and low-complexity models with particle swarm optimization) shows a good balance between accuracy and complexity with small databases, but gets stuck in local minima in high-dimensional databases. This work presents a new hybrid methodology to solve this problem. First, we incorporated to PSO-PARSIMONY an aggressive mutation strategy to encourage parsimony. Second, a hybrid method between PSO and genetic algorithms was also implemented. With these changes, particularly with the second one, improvements were observed in the search for more accurate and low-complexity models in high-dimensional databases.
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Acknowledgements
We are greatly indebted to Banco Santander for the REGI2020/41 fellowship. This study used the Beronia cluster (Universidad de La Rioja), which is supported by FEDER-MINECO grant number UNLR-094E-2C-225. The work is also supported by grant PID2020-116641GB-I00 funded by MCIN/ AEI/ 10.13039/501100011033.
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Divasón, J., Pernia-Espinoza, A., Martinez-de-Pison, F.J. (2022). New Hybrid Methodology Based on Particle Swarm Optimization with Genetic Algorithms to Improve the Search of Parsimonious Models in High-Dimensional Databases. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_29
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