Abstract
An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NP-complete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often yield nearly multiplicative speedups.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hauschild, M.W., Pelikan, M.: An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation 1(3), 111–128 (2011)
Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer, Boston (2002)
Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21(1), 5–20 (2002)
Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer (2005)
Hauschild, M.W., Pelikan, M., Sastry, K., Goldberg, D.E.: Using previous models to bias structural learning in the hierarchical BOA. Evolutionary Computation 20(1), 135–160 (2012)
Mühlenbein, H., Mahnig, T., Rodriguez, A.O.: Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics 5, 215–247 (1999)
Mühlenbein, H., Mahnig, T.: Evolutionary optimization and the estimation of search distributions with applications to graph bipartitioning. International Journal of Approximate Reasoning 31(3), 157–192 (2002)
Baluja, S.: Incorporating a priori knowledge in probabilistic-model based optimization. In: Pelikan, Sastry, Cantú-Paz (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, pp. 205–219. Springer (2006)
Schwarz, J., Ocenasek, J.: A problem-knowledge based evolutionary algorithm KBOA for hypergraph partitioning. In: Proc. of the Fourth Joint Conf. on Knowledge-Based Software Engineering, Brno, Czech Rep., pp. 51–58 (2000)
Hauschild, M.W., Pelikan, M., Sastry, K., Lima, C.F.: Analyzing probabilistic models in hierarchical BOA. IEEE Transactions on Evolutionary Computation 13(6), 1199–1217 (2009)
Pelikan, M., Hauschild, M.: Distance-based bias in model-directed optimization of additively decomposable problems. MEDAL Report No. 2012001, Missouri Estimation of Distribution Algorithms Laboratory, University of Missouri–St. Louis, St. Louis, MO (2012)
Pelikan, M., Sastry, K., Goldberg, D.E.: Sporadic model building for efficiency enhancement of the hierarchical BOA. Genetic Programming and Evolvable Machines 9(1), 53–84 (2008)
Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Genetic and Evol. Comp. Conf (GECCO 2001), pp. 511–518 (2001)
Chickering, D.M., Heckerman, D., Meek, C.: A Bayesian approach to learning Bayesian networks with local structure. Technical Report MSR-TR-97-07, Microsoft Research, Redmond, WA (1997)
Harik, G.R.: Finding multimodal solutions using restricted tournament selection. In: Proc. of the Int. Conf. on Genetic Algorithms (ICGA 1995), pp. 24–31 (1995)
Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: Jordan, M.I. (ed.) Graphical Models, pp. 421–459. MIT Press (1999)
Cooper, G.F., Herskovits, E.H.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Lima, C.F., Lobo, F.G., Pelikan, M., Goldberg, D.E.: Model accuracy in the Bayesian optimization algorithm. Soft Computing 15(7), 1351–1371 (2011)
Hauschild, M., Pelikan, M.: Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 417–427. Springer, Heidelberg (2008)
Hauschild, M.W., Pelikan, M.: Intelligent bias of network structures in the hierarchical BOA. In: Genetic and Evol. Comp. Conf. (GECCO 2009), pp. 413–420 (2009)
Pratt, L.Y., Mostow, J., Kamm, C.A., Kamm, A.A.: Direct transfer of learned information among neural networks. In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 584–589 (1991)
Caruana, R.: Multitask learning. Machine Learning 28, 41–75 (1997)
Pelikan, M., Hartmann, A.K.: Searching for ground states of Ising spin glasses with hierarchical BOA and cluster exact approximation. In: Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer (2006)
Young, A. (ed.): Spin glasses and random fields. World Scientific, Singapore (1998)
Pelikan, M., Kalapala, R., Hartmann, A.K.: Hybrid evolutionary algorithms on minimum vertex cover for random graphs. In: Genetic and Evol. Comp. Conf. (GECCO 2007), pp. 547–554 (2007)
Weigt, M., Hartmann, A.K.: Minimal vertex covers on finite-connectivity random graphs: A hard-sphere lattice-gas picture. Physical Review E 63, 056127 (2001)
Pelikan, M., Goldberg, D.E.: Hierarchical BOA Solves Ising Spin Glasses and Maxsat. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1271–1282. Springer, Heidelberg (2003)
Gent, I., Hoos, H.H., Prosser, P., Walsh, T.: Morphing: Combining structure and randomness. In: Proc. of the American Association of Artificial Intelligence (AAAI 1999), pp. 654–660 (1999)
Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. Master’s thesis, University of Illinois at Urbana-Champaign, Department of General Engineering, Urbana, IL (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pelikan, M., Hauschild, M.W., Lanzi, P.L. (2012). Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-32937-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32936-4
Online ISBN: 978-3-642-32937-1
eBook Packages: Computer ScienceComputer Science (R0)