Mateda_references.bib

@Article{Irurozki_et_al:2018,
  title={Algorithm 989: perm\_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems}
,
  author={Ekhine  Irurozki and Josu Ceberio and Josean Santamaria and Roberto Santana and Alexander Mendiburu},
  journal={ACM Transactions on Mathematical Software (TOMS)},
  volume={44},
  number={4},
  pages={47},
  year={2018},
  publisher={ACM},
}


@TechReport{Santana_et_al:2009a,
  author =       "R. Santana and C. Echegoyen  and  A. Mendiburu and  C. Bielza and J. A. Lozano and P. Larra{\~{n}}aga and R. Arma{\~n}anzas and S. Shakya",
  title =        "{MATEDA}: A suite of {EDA} programs in {M}atlab",
  year = "2009",
  number =   "EHU-KZAA-IK-2/09",
  month = "February",
  institution = "Department of Computer Science and Artificial Intelligence, University of the Basque Country",
  abstract = {This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools.
},
  url = "http://hdl.handle.net/10810/4622",
 }


@Article{Santana_et_al:2009h,
  author =  "R. Santana and  C. Bielza and P. Larra{\~{n}}aga and J. A. Lozano and C. Echegoyen  and  A. Mendiburu and   R. Arma{\~n}anzas and S. Shakya",
  title = "{Mateda-2.0}: A {MATLAB} package for the implementation and analysis of estimation of distribution algorithms",
  journal ={Journal of Statistical Software},
  publisher ={American Statistical Association},
  year = "2010",
  volume = "35",
  number = "7",
  pages = {1-30},
  abstract = {This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.},
  url = {http://www.jstatsoft.org/v35/i07},
 }


@InProceedings{Santana_et_al:2011b,
  author =  {R. Santana and C. Bielza and  P. Larra{\~{n}}aga},
  title =   {Affinity propagation enhanced by estimation of distribution algorithms},
  pages =   {331-338},
  booktitle =   {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011},
  year =    2011,
  address =  {Dublin, Ireland},
  abstract = {Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.},
  url = {http://dl.acm.org/citation.cfm?id=2001622},
 }


@InProceedings{Santana_et_al:2013a,
 author =       "R. Santana and R. I. McKay and J. A. Lozano",
 title =        "Symmetry in evolutionary and estimation of distribution algorithms",
 booktitle =   {Proceedings of the 2013 Congress on Evolutionary Computation CEC-2013},
 year      = {2013},
 publisher = {IEEE Press},
 address   = {Cancun, Mexico},
 pages = {2053-2060}
 }