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Evolutionary computation based on Bayesian classifiers

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Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier-either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one-is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.








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  • Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain
  • Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain
  • Department of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain


  • Baluja S. (1994): Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. — Techn. Rep., Carnegie Mellon, CMU-CS-94-163.
  • Baluja S. and Davies S. (1997): Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space.—Techn. Rep., Carnegie Mellon, CMUCS- 97-107.
  • Cantu-Paz E. (2001): Supervised and unsupervised discretization methods for evolutionary algorithms. — Proc. Genetic and Evolutionary Computation Conference (GECCO’2001), Workshop Optimization by Building and Using Probabilisitic Models, San Francisco, California, pp. 213–216.
  • Cestnik B., Kononenko I. and Bratko I. (1987): ASSISTANT-86: A knowledge elicitation tool for sophisticated users, In: Progress in Machine Learning (I. Bratko and N. Lavrac, Eds.). —Wilmslow, U.K.: Sigma Press, pp. 31–45.
  • Cheng J. and Greiner R. (1999): Comparing Bayesian network classifiers. — Proc. 15th Conf. Uncertainty in Artificial Intelligence, San Francisco, CA: Morgan Kaufmann Publishers, pp. 101–107.
  • Chow C. and Liu C. (1968): Approximating discrete probability distributions with dependence trees. — IEEE Trans. Inf. Theory, Vol. 14, No. 3, pp. 462–467.
  • de Bonet J.S., Isbell C.L. and Viola P. (1997): MIMIC: Finding optima by estimating probability densities, In: Advances in Neural Information Processing Systems (M. Mozer, M. Jordan and Th. Petsche, Eds.).—Cambridge, MA: The MIT Press, Vol. 9, pp. 424–431.
  • Domingos P. and Pazzani M. (1997): On the optimality of the simple Bayesian classifier under zero-one loss. — Mach. Learn., Vol. 29, No. 2–3, pp. 103–130.
  • Duda R. and Hart P. (1973): Pattern Classification and Scene Analysis. — New York: Wiley.
  • Etxeberria R. and Larrañaga P. (1999): Global optimization with Bayesian networks. — Proc. 2nd Symp. Artificial Intelligence, CIMAF99, La Habana, Cuba, pp. 332–339.
  • Friedman N., Geiger D. and Goldsmidt M. (1997): Bayesian network classifiers.—Mach. Learn., Vol. 29, No. 2, pp. 131– 163.
  • Gammerman A. and Thatcher A.R. (1991): Bayesian diagnostic probabilities without assuming independence of symptoms. —Meth. Inf. Medic., Vol. 30, No. 1, pp. 15–22.
  • Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. — Reading: Addison- Wesley.
  • Harik G. (1999): Linkage learning via probabilistic modeling in the EcGA. — Techn. Rep., University of Illinois, Urbana, IlliGAL Report No. 99010.
  • Harik G., Lobo F.G. and Golberg D.E. (1998): The compact genetic algorithm. — Proc. IEEE Conf. Evolutionary Computation, Piscataway, NJ, pp. 523–528.
  • Holland J.H. (1975): Adaptation in Natural and Artificial Systems. —Michigan: The University of Michigan Press.
  • Inza I., Larrañaga P., Etxeberria R. and Sierra B. (2000): Feature subset selection by Bayesian network-based optimization. —Artif. Intell., Vol. 123, No. 1–2, pp. 157–184.
  • Kaufman K. and Michalski R. (1999): The AQ18 machine learning and data mining system: An implementation and user’s guide. — Techn. Rep., Machine Learning and Inference Laboratory, George Manson University, Fairfax, Virginia.
  • Kohavi R. and John G. (1997): Wrappers for feature subset selection. —Artif. Intell., Vol. 97, No. 1–2, pp. 273–324.
  • Kononenko I. (1990): Comparison of inductive and naïve Bayesian learning approaches to automatic knowledge acquisition, In: Current Trends in Knowledge Acquisition (B. Wielinga, J. Boose, B. Gaines, G. Shereiber and M. van Someren, Eds.). — Amsterdam: IOS Press, pp. 190–197.
  • Kononenko I. (1991): Semi-naïve Bayesian classifiers. — Proc. 6th Europ. Working Session on Learning, Porto, Portugal, pp. 206–219.
  • Kontkanen P., Myllymäki P., Tirri H. and Valtonen K. (2000): Bayesian multinet classifiers. — Proc. 10th Int. Conf. Computing and Information (ICCI’2000).
  • Langley P. and Sage S. (1994): Induction of selective Bayesian classifiers. — Proc. 10th Conf. Uncertainty in Artificial Intelligence, Seattle, WA, pp. 399–406.
  • Larrañaga P. and Lozano J.A. (2001): Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation.— Kluwer.
  • Liu H. and Motoda H. (1998): Feature Selection. From Knowledge Discovery and Data Mining.— Boston: Kluwer.
  • Llorà X. and Goldberg D.E. (2003): Wise Breeding GA via Machine Learning Techniques for Function Optimization. — Proc. Genetic and Evolutionary Computation Conference GECCO-03, Part I, Chicago, Illinois, pp. 1172–1183.
  • Michalewicz Z. (1992): Genetic Algorithms + Data Structures = Evolution Programs. — Berlin: Springer Verlag.
  • Michalski R.S. (2000): Learnable execution model: Evolutionary processes guided by machine learning. — Mach. Learn., Vol. 38, pp. 9–40.
  • Minsky M. (1961): Steps toward artificial intelligence.—Trans. Inst. Radio Eng., Vol. 49, pp. 8–30.
  • Mühlenbein H. (1998): The equation for response to selection and its use for prediction. — Evolut. Comput., Vol. 5, No. 3, pp. 303–346.
  • Mühlenbein H. and Mahning T. (1999): FDA—A scalable evolutionary algorithm for the optimization of additively decomposed functions. — Evolut. Comput., Vol. 7, No. 4, pp. 353–376.
  • Mühlenbein H., Mahning T. and Ochoa A. (1999): Schemata, distributions and graphical models in evolutionary optimization. —J. Heurist., Vol. 5, pp. 215–247.
  • Mühlenbein H. and Paaß G. (1996): From recombination of genes to the estimation of distributions, I. Binary parameters, In: Parallel Problem Solving from Nature—PPSN IV (M. Voigt, W. Ebeling, I. Rechenberg and H.-P. Schwefel, Eds.).—Lecture Notes in Computer Science 1411, Berlin: Springer, pp. 178–187.
  • Muñoz A. (2003): LEM algorithms.—Final year project, Computer Engineering Faculty, University of the Basque Country, (in Spanish).
  • Neapolitan R.E. (2003): Learning Bayesian Networks. — Prentice Hall.
  • Ohmann C., Yang Q., Kunneke M., Stolzing H., Thon K. and Lorenz W. (1988): Bayes theorem and conditional dependence of symptoms: Different models applied to data of upper gastrointestinal bleeding. — Meth. Inf. Medic., Vol. 27, No. 2, pp. 73–83.
  • Pazzani M. (1997): Searching for dependencies in Bayesian classifiers, In: Learning from Data: Artificial Intelligence and Statistics V (D. Fisher and H.-J. Lenz, Eds.). — New York: Springer, pp. 239–248.
  • Pelikan M., Goldberg D.E. and Cantú-Paz E. (1999): BOA: The Bayesian optimization algorithm. — Proc. Genetic and Evolutionary Computation Conference GECCO-99, Orlando, pp. 525–532.
  • Pelikan M. and Mühlenbein H. (1999): The bivariate marginal distribution algorithm, In: Advances in Soft Computing- Engineering Design and Manufacturing (R. Roy, T. Furuhashi and P.K. Chandhory, Eds.). — London: Springer- Verlag, pp. 521–535.
  • Sahami M. (1996): Learning limited dependence Bayesian classifiers. — Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining, Portland, OR, pp. 335–338.
  • Santana R. (2004): Estimation of distribution algorithms with Kikuchi approximations. —(submitted).
  • Syswerda G. (1993): Simulated crossover in genetic algorithms, In: Foundations of Genetic Algorithms (L.D. Whitley, Ed.). — Vol. 2, San Mateo: Morgan Kaufmann, pp. 239– 255.
  • Thierens D. and Bosman P.A.N. (2001): Multi-objective mixture-based iterated density estimation evolutionary algorithms.— Proc. Genetic and Evolutionary Computation Conference, GECCO’2001, San Francisco, pp. 663–670.
  • Todd B.S. and Stamper R. (1994): The relative accuracy of a variety of medical diagnostic programs. — Meth. Inf. Medic., Vol. 33, No. ??, pp. 402–416.
  • Ventura S., Herrera F., Berná J.N. and Hervás C. (2002): Evolución guiada mediante aprendizaje. Comparación en problemas de optimización. — Proc. Conf. Algoritmos Evolutivos y Bioinspirados, AEB’02, pp. 430–436, (in Spanish).
  • Whitley D. and Kauth J. (1988): GENITOR: A different genetic algorithm. — Proc. Rocky Mountain Conf. Artificial Intelligence, USA, Vol. 2, pp. 118–130.
  • Zitzler E., Deb K. and Thiele L. (1999): Comparison of multiobjective evolutionary algorithms on test functions of different difficulty. — Proc. 1999 Genetic and Evolutionary Computation Conf., Orlando, FL, pp.121–122.

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