In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. IEEE World Congress on Computational Intelligence (Cat. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. Sanz-Garcia, A., Fernandez-Ceniceros, J., Antonanzas-Torres, F., Pernia-Espinoza, A., Martinez-de Pison, F.J.: GA-parsimony: a GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace. Recent Advancements in Hybrid Artificial Intelligence Systems Martinez-de Pison, F.J., Gonzalez-Sendino, R., Aldama, A., Ferreiro-Cabello, J., Fraile-Garcia, E.: Hybrid methodology based on Bayesian optimization and GA-parsimony to search for parsimony models by combining hyperparameter optimization and feature selection. Martinez-de Pison, F.J., Ferreiro, J., Fraile, E., Pernia-Espinoza, A.: A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R package. Pernía-Espinoza, A., Fernandez-Ceniceros, J., Antonanzas, J., Urraca, R., Martinez-de Pison, F.J.: Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components. McKay, M.D., Beckman, R.J., Conover, W.J.: Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Martinez-de-Pison, F.J.: GAparsimony: Searching Parsimony Models with Genetic Algorithms (2019). Ma, B., Xia, Y.: A tribe competition-based genetic algorithm for feature selection in pattern classification. Li, H., Shu, D., Zhang, Y., Yi, G.Y.: Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. Kennedy, J., Eberhart, R.: Particle swarm optimization. Part 2: Parsimonious soft-computing-based metamodel. įernandez-Ceniceros, J., Sanz-Garcia, A., Antoñanzas-Torres, F., Martinez-de Pison, F.J.: A numerical-informational approach for characterising the ductile behaviour of the t-stub component. Part 1: Refined finite element model and test validation. In: Proceedings of the 2000 Congress on Evolutionary Computation. 17, December 2006Įberhart, R., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. 32, 23–37 (2015)Ĭlerc, M.: Stagnation Analysis in Particle Swarm Optimisation or What Happens When Nothing Happens, p. KeywordsĪhila, R., Sadasivam, V., Manimala, K.: An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. The new method needed more iterations and obtained slightly more complex individuals, but it performed better in the search for accurate models. Then, the new proposal was compared with GA-PARSIMONY, our previously published methodology that uses genetic algorithms in the optimization process. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. To evaluate the new proposal, a comparative study with Multilayer Perceptron algorithm was performed by applying it to predict three important parameters of the force-displacement curve in T-stub steel connections: initial stiffness, maximum strength, and displacement at failure. PSO-PARSIMONY uses automatic hyperparameter optimization and feature selection to search for accurate models with low complexity. We present PSO-PARSIMONY, a new methodology to search for parsimonious and highly accurate models by means of particle swarm optimization.
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