JabRef references

Ochoa A, Soto MR, Santana R, Madera J and Jorge N (1999), "The Factorized Distribution Algorithm and the Junction Tree: A Learning Perspective", In Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). Havana, Cuba, March, 1999. , pp. 368-377. Editorial Academia. Havana, Cuba.

[Abstract] [BibTeX] [URL]

Abstract: This paper extends the FDA - the Factorized Distribution Algorithm - with a structural learning component.
The FDA has been extensively investigated for the optimization of additively decomposed discrete functions
(ADFs). Now, we are able to deal with more general problems, which are solved by FDA in a blackbox
optimization scenario. The key point is the construction of the Junction Tree, which is placed at the centre of
the algorithm. Learning the Junction Tree directly from the data is a process that is accomplished by making
independency tests of as lower as possible order. The proposed algorithm belongs to the class of Estimation
Distribution Algorithms and represents an interesting alternative to approach the Linkage Problem in Genetic
Algorithms.

BibTeX:

@inproceedings{Ochoa_et_al:1999,
  author = {A. Ochoa and M. R. Soto and R. Santana and J. Madera and N. Jorge},
  editor = {A. Ochoa and M. R. Soto and R. Santana},
  title = {The Factorized Distribution Algorithm and the Junction Tree: A Learning Perspective},
  booktitle = {Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99)},
  publisher = {Editorial Academia. Havana, Cuba},
  year = {1999},
  pages = {368--377},
  url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/deo012.pdf}
}

de León EP and Santana R (1999), "A hybrid genetic algorithm for a Hamiltonian path problem", Revista Investigación Operacional. Vol. 20(1), pp. 20-29. Universidad de la Habana.

[Abstract] [BibTeX]

Abstract: This paper deals with Hável and Erdös conjecture that asserts that all bipartite graph formed with the middle levels of n-dimensional cube is Hamiltonian, when n is odd. This means that at least one Hamiltonian cycle should be found in the graph.
We use a circular representation of the vertices of n-dimensional cube which makes it possible to introduce two group actions in order to reduce the problem to find out a Hamiltonian path in multi - level quotient graphs.
We report on a hybrid genetic algorithm for the Hável and Erdös conjecture problem. We present interesting results which show that this GA approach gives optimal solutions in multi-level quotient graph. We use bitstring representation, an evolutionary fitness function, restricted edge crossover operator, intelligent mutation, seeding and adaptive mutation rate. Three Hamiltonian paths are constructed with this method.

BibTeX:

@article{Ponce_and_Santana:1999,
  author = {E. Ponce de León and R. Santana},
  title = {A hybrid genetic algorithm for a Hamiltonian path problem},
  journal = {Revista Investigación Operacional},
  publisher = {Universidad de la Habana},
  year = {1999},
  volume = {20},
  number = {1},
  pages = {20--29}
}

Rivera JP and Santana R (1999), "Improving the discovery component of classifier systems by the application of estimation of distribution algorithms", In Proceedings of the students sessions, ACAI'99. Chania, Greece , pp. 43-44.

[Abstract] [BibTeX] [URL]

Abstract: This paper reports preliminary results of a Classifier Systems that uses an EDA as its discovery component. We have introduced a new method to estimate the prediction of descendant, and explained measures that help to a deeply understanding of the XCS performance. Further work includes the analysis of other classifier systems for more complex environments that would need discovery components able to exploit in a greater measure particular characteristics of the environment.

BibTeX:

@inproceedings{Rivera_and_Santana:1999,
  author = {J. P. Rivera and R. Santana},
  title = {Improving the discovery component of classifier systems by the application of estimation of distribution algorithms},
  booktitle = {Proceedings of the students sessions, ACAI'99},
  year = {1999},
  pages = {43-44},
  url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/Rivera1999a.pdf}
}

Santana R and Ochoa A (1999), "A Constraint Univariate Marginal Distribution Algorithm". Research Report at: Institute of Cybernetics, Mathematics and Physics. Havana, Cuba (ICIMAF 99-76, CENIA 99-04)

[Abstract] [BibTeX] [URL]

Abstract: This paper proposes a new optimization algorithm to deal with binary constraint
problems. The algorithm is based in the Univariate Marginal Distribution Algorithm.
We apply our approach to the optimization of functions with different characteristics.
For the test functions considered we show the superiority of our algorithm to traditional
population search methods that have been used to solve these kind of problems. We
report some particular features exhibited by the algorithm and discuss extensions that
could make of it a still more powerful optimization tool.

BibTeX:

@techreport{Santana_and_Ochoa:1999,
  author = {R. Santana and A. Ochoa},
  title = {A Constraint Univariate Marginal Distribution Algorithm},
  school = {Institute of Cybernetics, Mathematics and Physics},
  year = {1999},
  number = {ICIMAF 99-76, CENIA 99-04},
  url = {https://www.researchgate.net/publication/333718972_A_Constraint_Univariate_Marginal_Distribution_Algorithm}
}

Santana R and Ochoa A (1999), "On Estimation Distribution Algorithms", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, Workshop Program. Orlando, FL , pp. 402. Morgan Kaufmann Publishers, San Francisco, CA.

[Abstract] [BibTeX]

Abstract: The main goal of the dissertation research is the design of efficient Estimation Distribution Algorithms based on non simply connected probabilistic networks. This work is part of a more ambitious ongoing research on Low Cost Evolutionary Algorithms (LCEA). Other important areas to be covered with the dissertation are: 1) The integration of different theories about GA in order to explain successful results of EDA in the context of evolutionary computation. 2) Define ways of incorporating knowledge about the problem domain, prior to the beginning and during the function optimization . 3) To study the recognized sources of hardness for GA’s in the framework of EDA. 4) Define measures to evaluate the quality of the different search strategies used. We now describe some aspect of the current research in these topics.

BibTeX:

@inproceedings{Santana_and_Ochoa:1999a,
  author = {R. Santana and A. Ochoa},
  editor = {Wu A. S.},
  title = {On Estimation Distribution Algorithms},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, Workshop Program},
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  year = {1999},
  pages = {402}
}

Santana R and Ochoa A (1999), "Dealing with constraints with estimation of distribution algorithms: The univariate case", In Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). Havana, Cuba, March, 1999. , pp. 378-384.

[Abstract] [BibTeX] [URL]

BibTeX:

@inproceedings{Santana_and_Ochoa:1999b,
  author = {R. Santana and A. Ochoa},
  editor = {A. Ochoa and M. R. Soto and R. Santana},
  title = {Dealing with constraints with estimation of distribution algorithms: The univariate case},
  booktitle = {Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99)},
  year = {1999},
  pages = {378-384},
  url = {https://www.researchgate.net/publication/333718972_A_Constraint_Univariate_Marginal_Distribution_Algorithm}
}

Santana R, de León EP and Ochoa A (1999), "The incident edge model", In Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). Havana, Cuba, March, 1999. , pp. 352-359.

[Abstract] [BibTeX] [URL]

Abstract: In this paper we introduce the incident edge model, a fitness function model for a large set of problems defined on graphs. The model can be used to define functions whose optimization led to the finding of different structures on graphs. The model can also be useful to determine the best optimization algorithm for a given problem. As an example of the application of our model we describe an optimization approach for the problem of finding the dissection of a graph. For the optimization of this additively decomposable function we use the Factorized Distribution Algorithm. We focus on the way that different factorizations of the probability distribution can influence the behavior of the Factorized Distribution Algorithm for the dissection problem.

BibTeX:

@inproceedings{Santana_et_al:1999,
  author = {R. Santana and E. Ponce de León and A. Ochoa},
  editor = {A. Ochoa and M. R. Soto and R. Santana},
  title = {The incident edge model},
  booktitle = {Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99)},
  year = {1999},
  pages = {352-359},
  url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/cimaf5.pdf}
}

Santana R, Ochoa A and Soto MR (1999), "Evolutionary Algorithms for Dynamic Optimization Problems: An approach using Evolutionary Theory and the Incident Edge Model", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, Workshop Program. Orlando, FL , pp. 149-152. Morgan Kaufmann Publishers, San Francisco, CA.

[Abstract] [BibTeX]

Abstract: In this paper we analyze the use of Evolutionary Algorithms (EAs) for Dynamic Optimization Problems (DOP). We show how research on Evolutionary Theory can throw light to the question of characterizing dynamic problems. We present arguments in favor of using fitness function models as benchmark for the study of DOP. As an example we present the Incident Edge Model, and discuss how different kinds of dynamics for problems defined on graphs can be translated to the model. Finally we apply an EA, the Constraint Univariate Marginal Distribution Algorithm, for the problem of finding the spanning trees of graphs that change through time. We show that efficient EAs should be able to employ information about changes in the environment to guide the search.

BibTeX:

@inproceedings{Santana_et_al:1999a,
  author = {R. Santana and A. Ochoa and M. R. Soto},
  editor = {A. S. Wu},
  title = {Evolutionary Algorithms for Dynamic Optimization Problems: An approach using Evolutionary Theory and the Incident Edge Model},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, Workshop Program},
  publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
  year = {1999},
  pages = {149--152}
}

Santana R, Ochoa A and Soto MR (2001), "On the use of Factorized Distribution Algorithms for problems defined on graphs", In Electronic Notes in Discrete Mathematics. Vol. 8 Elsevier.

[Abstract] [BibTeX] [URL]

Abstract: This short paper surveys current work on the use of Factorized Distribution Algorithms for the solution of combinatorial optimization problems defined on graphs. We also advance a number of approaches for future work along this line.

BibTeX:

@inproceedings{Santana_et_al:2001e,
  author = {Roberto Santana and Alberto Ochoa and Marta R. Soto},
  editor = {Hajo Broersma and Ulrich Faigle and Johann Hurink and Stefan Pickl},
  title = {On the use of Factorized Distribution Algorithms for problems defined on graphs},
  booktitle = {Electronic Notes in Discrete Mathematics},
  publisher = {Elsevier},
  year = {2001},
  volume = {8},
  url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/FDA_Graphs.pdf}
}

Santana R (1999), "Towards an intelligent genetic search: Defining measures of convergence", In Proceedings of the students sessions, ACAI'99. Chania, Greece , pp. 41-42.

[Abstract] [BibTeX] [URL]

Abstract: The approach we discuss here is in line with techniques that change or adapt the parameter values as the search progresses, nevertheless it exhibits two main differences with previous work on this topic. First, the analysis has been thought to be applied not only to genetic algorithms (GAs) but also to other Population Based Search Methods that use Selection (PBSMS). Second, adaptation is achieved by considering rules able to change the application of different operators along the search, and not only by adapting the parameters. For reasons of space we concentrate here in the question of defining measures that could allow to a PBSMS to receive a feedback about its own behavior, and use this information in the next step of the algorithm to improve the search.

BibTeX:

@inproceedings{Santana:1999,
  author = {R. Santana},
  title = {Towards an intelligent genetic search: Defining measures of convergence},
  booktitle = {Proceedings of the students sessions, ACAI'99},
  year = {1999},
  pages = {41-42},
  url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/[Santana,1999].pdf}
}

Soto MR, Ochoa A, Madera J and Santana R (1999), "Estructuras gráficas simples para algoritmos evolutivos", In Proceedings of the Segundo Encuentro Nacional de Computacionón ENC-99. Pachuca, México , pp. 79-84.

[BibTeX]

BibTeX:

@inproceedings{Soto_et_al:1999a,
  author = {M. R. Soto and A. Ochoa and J. Madera and R. Santana},
  title = {Estructuras gráficas simples para algoritmos evolutivos},
  booktitle = {Proceedings of the Segundo Encuentro Nacional de Computacionón ENC-99},
  year = {1999},
  pages = {79--84}
}