AlbaCabrera E, Santana R, Ochoa A and LazoCortés M (2000), "Finding typical testors by using an evolutionary strategy", In Proceedings of the Fith Ibero American Symposium on Pattern Recognition. Lisbon, Portugal , pp. 267278.
[Abstract] [BibTeX] [URL]

Abstract: The concept of testor appeared in the middle of the fifties. Testors and particularly typical testors, have been used in feature selection and supervised classification problems. Deterministic algorithms have usually been used to find typical testors. In this paper a new approach to find typical testors of a basic matrix is described. This approach is based on the application of the Univariate Marginal Distribution Algorithm as the kernel of a search strategy. The behavior of this algorithm is at least as well as the simple Genetic Algorithms with uniform crossover for the same kind of problems, but it is simpler and less costly in computational terms. Several experiments confirm the validity of this approach. 
BibTeX:
@inproceedings{Alba_et_al:2000,
author = {Eduardo AlbaCabrera and R. Santana and Alberto Ochoa and Manuel LazoCortés},
title = {Finding typical testors by using an evolutionary strategy},
booktitle = {Proceedings of the Fith Ibero American Symposium on Pattern Recognition},
year = {2000},
pages = {267278},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/2000/testores}
}

Pereira FB, Machado P, Costa E, Cardoso A, Ochoa A, Santana R and Soto MR (2000), "Too busy to learn", In Proceedings of the 2000 Congress on Evolutionary Computation CEC2000. La Jolla Marriott Hotel La Jolla, California, USA, July, 2000. , pp. 720727. IEEE Press.
[Abstract] [BibTeX] [URL]

Abstract: The goal of this research is to analyze how individual learning helps an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and very prone to premature convergence search spaces, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported. When the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost 
BibTeX:
@inproceedings{Pereira_et_al:2000,
author = {Francisco B. Pereira and Penousal Machado and Ernesto Costa and Amílcar Cardoso and Alberto Ochoa and Roberto Santana and Marta Rosa Soto},
title = {Too busy to learn},
booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation CEC2000},
publisher = {IEEE Press},
year = {2000},
pages = {720727},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/2000/CEC2000.pdf}
}

Pereira FB, Machado P, Costa E, Cardoso A, Ochoa A, Santana R and Soto MR (2000), "Too Busy to Learn", In Colectânea de Comunicacões. Instituto Politécnico de Coimbra , pp. 699712. Ediliber, Lda..
[Abstract] [BibTeX] [URL]

Abstract: The goal of this research is to analyze how individual learning helps an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and very prone to premature convergence search spaces, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported. When the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost 
BibTeX:
@incollection{Pereira_et_al:2000a,
author = {Francisco B. Pereira and Penousal Machado and Ernesto Costa and Amílcar Cardoso and Alberto Ochoa and Roberto Santana and Marta Rosa Soto},
title = {Too Busy to Learn},
booktitle = {Colectânea de Comunicacões},
publisher = {Ediliber, Lda.},
year = {2000},
pages = {699712},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/2000/CEC2000.pdf}
}

Rivera JP and Santana R (2000), "Design of an algorithm based on the estimation of distributions to generate new rules in the XCS classifier system". Research Report at: Institute of Cybernetics, Mathematics and Physics. Havana, Cuba, June, 2000. (ICIMAF 2000100, CEMAFIT 200078)
[Abstract] [BibTeX]

Abstract: In classifier systems the genetic algorithms (GAs) have been usually employed as the discovery component. The theory of evolutionary algorithms has achieved important results nowadays, but classifier systems do not seem to be employing these advances in their own benefit. The aim of this paper is to analyze the effect of replacing the traditional discovery component of the XCS classifier system by another kind of population based search method, an Estimation Distribution Algorithm (EDA). The algorithm, which we have called CSEDA required the implementation of a mutationlike effect with a selected mutation rate. To achieve a proper performance of XCS a new rule deletion method was developed. A more elaborated technique for the calculation of the predictions of the offspring was devised. Finally, to obtain a categorical comparison between both evolutionary algorithms it was necessary to define performance measures that permitted us to verify that the proposed algorithm performed better than the GA for the examples considered. 
BibTeX:
@techreport{Rivera_and_Santana:2000,
author = {J. P. Rivera and R. Santana},
title = {Design of an algorithm based on the estimation of distributions to generate new rules in the XCS classifier system},
school = {Institute of Cybernetics, Mathematics and Physics},
year = {2000},
number = {ICIMAF 2000100, CEMAFIT 200078}
}

Santana R, Pereira FB, Costa E, Ochoa A, Machado P, Cardoso A and Soto MR (2000), "Probabilistic Evolution and the busy beaver Problem", In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference GECCO2000. Las Vegas, Nevada, USA, 8 July, 2000. , pp. 261268.
[Abstract] [BibTeX] [URL]

Abstract: We discuss the use of probabilistic evolution in an important class of problems based on Turing Machines, namely the famous Busy Beaver. Despite the bad properties of this problem for a probabilistic solution: nonbinary representation and variable associations with a strongly connected graphlike structure, our algorithm seems to outperform previous evolutionary computation approaches. 
BibTeX:
@inproceedings{Santana_et_al:2000,
author = {Roberto Santana and Francisco B. Pereira and Ernesto Costa and Alberto Ochoa and Penousal Machado and Amilcar Cardoso and Marta Rosa Soto},
editor = {Darrell Whitley},
title = {Probabilistic Evolution and the busy beaver Problem},
booktitle = {Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference GECCO2000},
year = {2000},
pages = {261268},
url = {https://cdv.dei.uc.pt/wpcontent/uploads/2014/03/sors+00.pdf}
}

Santana R, Ochoa A and Soto MR (2000), "A Factorized Distribution Algorithm of bounded complexity for integer problems". Research Report at: Institute of Cybernetics, Mathematics and Physics. Havana, Cuba, December, 2000. (ICIMAF 2000, CENIA 2000)
[Abstract] [BibTeX] [URL]

Abstract: A Factorized Distribution algorithm that use up to pairwise dependencies for the optimization of integer problems is introduced. Our proposal combines classical methods for structural learning of dependencies with a procedure that approximates the bivariate marginals by sampling the data using auxiliary tables. The algorithm overperforms the Univariate Marginal Distribution Algorithm for the integer problems tested. 
BibTeX:
@techreport{Santana_et_al:2000a,
author = {R. Santana and A. Ochoa and M. R. Soto},
title = {A Factorized Distribution Algorithm of bounded complexity for integer problems},
school = {Institute of Cybernetics, Mathematics and Physics},
year = {2000},
number = {ICIMAF 2000, CENIA 2000},
url = {https://www.researchgate.net/profile/RobertoSantana/publication/269105263_A_Factorized_Distribution_Algorithm_of_bounded_complexity_for_integer_problems/links/5d00b83092851c874c5fcee8/AFactorizedDistributionAlgorithmofboundedcomplexityforintegerproblems.pdf}
}

Santana R, Pereira FB, Costa E, Ochoa A, Machado P, Cardoso A and Soto MR (2000), "Probabilistic Evolution and the Busy Beaver Problem", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO2000. Las Vegas, Nevada, USA, July, 2000. , pp. 380.
[Abstract] [BibTeX] [URL]

Abstract: We discuss the use of probabilistic evolution in an important class of problems based on Turing Machines, namely the famous Busy Beaver. Despite the bad properties of this problem for a probabilistic solution: nonbinary representation and variable associations with a strongly connected graphlike structure, our algorithm seems to outperform previous evolutionary computation approaches. 
BibTeX:
@inproceedings{Santana_et_al:2000b,
author = {Roberto Santana and Francisco B. Pereira and Ernesto Costa and Alberto Ochoa and Penousal Machado and Amilcar Cardoso and Marta Rosa Soto},
title = {Probabilistic Evolution and the Busy Beaver Problem},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO2000},
year = {2000},
pages = {380},
url = {https://dl.acm.org/doi/pdf/10.5555/2933718.2933781}
}
