New Hybrid Methodology Based on Particle Swarm Optimization with Genetic Algorithms to Improve the Search of Parsimonious Models in High-Dimensional Databases-Reference-Cited by-同舟云学术

New Hybrid Methodology Based on Particle Swarm Optimization with Genetic Algorithms to Improve the Search of Parsimonious Models in High-Dimensional Databases

Published:2022 Issue: Volume: Page:335-347
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Divasón Jose^ORCID,Pernia-Espinoza Alpha^ORCID,Martinez-de-Pison Francisco Javier^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-15471-3_29

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3. Engelbrecht, A.P.: Particle swarm optimization with crossover: a review and empirical analysis. Artif. Intell. Rev. 45(2), 131–165 (2015). https://doi.org/10.1007/s10462-015-9445-7

4. Hao, Z.F., Wang, Z.G., Huang, H.: A particle swarm optimization algorithm with crossover operator. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1036–1040 (2007)

5. Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) Foundations of Fuzzy Logic and Soft Computing, pp. 789–798. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72950-1_77

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