Domingo Ortiz-Boyer

Juan Antonio Romero del Castillo

Eng. Appl. Artif. Intell., 2022

2021

Instance selection for multi-label learning based on a scalable evolutionary algorithm.

[BibT_eX]

[DOI]

Proceedings of the 2021 International Conference on Data Mining, 2021

2012

Evolutionary algorithms and cross entropy.

[BibT_eX]

[DOI]

Colin Fyfe

Int. J. Knowl. Based Intell. Eng. Syst., 2012

Class imbalance methods for translation initiation site recognition in DNA sequences.

[BibT_eX]

[DOI]

Javier Pérez-Rodríguez

María D. García-Pedrajas

Colin Fyfe

Knowl. Based Syst., 2012

2011

An empirical study of binary classifier fusion methods for multiclass classification.

[BibT_eX]

[DOI]

Inf. Fusion, 2011

2010

A cooperative coevolutionary algorithm for instance selection for instance-based learning.

[BibT_eX]

[DOI]

Juan Antonio Romero del Castillo

Mach. Learn., 2010

Class Imbalance Methods for Translation Initiation Site Recognition.

[BibT_eX]

[DOI]

María D. García-Pedrajas

Colin Fyfe

Proceedings of the Trends in Applied Intelligent Systems, 2010

2009

Boosting k-nearest neighbor classifier by means of input space projection.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2009

2008

Robust confidence intervals applied to crossover operator for real-coded genetic algorithms.

[BibT_eX]

[DOI]

Soft Comput., 2008

Boosting random subspace method.

[BibT_eX]

[DOI]

Neural Networks, 2008

2007

A cooperative constructive method for neural networks for pattern recognition.

[BibT_eX]

[DOI]

Pattern Recognit., 2007

Improving crossover operator for real-coded genetic algorithms using virtual parents.

[BibT_eX]

[DOI]

J. Heuristics, 2007

2006

Improving Multiclass Pattern Recognition by the Combination of Two Strategies.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2006

An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization.

[BibT_eX]

[DOI]

Neural Networks, 2006

2005

Cooperative coevolution of artificial neural network ensembles for pattern classification.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2005

Analyzing the statistical features of CIXL2 crossover offspring.

[BibT_eX]

[DOI]

Soft Comput., 2005

CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features.

[BibT_eX]

[DOI]

Francisco J. Martínez-Estudillo

J. Artif. Intell. Res., 2005

Memetic Algorithms to Product-Unit Neural Networks for Regression.

[BibT_eX]

[DOI]

Alfonso C. Martínez-Estudillo

Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

Cascade Ensembles.

[BibT_eX]

[DOI]

Rafael del Castillo Gomariz

Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

Crossover effect over penalty methods in function optimization with constraints.

[BibT_eX]

[DOI]

Rafael del Castillo Gomariz

Proceedings of the IEEE Congress on Evolutionary Computation, 2005

2004

Cooperative coevolution of generalized multi-layer perceptrons.

[BibT_eX]

[DOI]

Neurocomputing, 2004

2003

Node Level Crossover Applied to Neural Network Evolution.

[BibT_eX]

[DOI]

Eloy Sanz-Tapia

Rafael del Castillo Gomariz

Proceedings of the Artificial Neural Nets Problem Solving Methods, 2003

Application of Crossover Operators Based on Confidence Interval in Modeling Problems Using Real-Coding Genetic Algorithms.

[BibT_eX]

[DOI]

Sebastián Ventura

Proceedings of the Current Topics in Artificial Intelligence, 2003

2002

Crossover Operator Effect in Function Optimization with Constraints.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature, 2002

Theoretical Analysis of the Confidence Interval Based Crossover for Real-Coded Genetic Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature, 2002

2001

Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks.

[BibT_eX]

[DOI]

Eloy Sanz-Tapia

Proceedings of the Connectionist Models of Neurons, 2001

Genetic algorithms with crossover based on confidence interval as an alternative to traditional nonlinear regression methods.

[BibT_eX]

[DOI]