Abstract
During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.
Similar content being viewed by others
Notes
In terms of complexity theory, finding the optimal PGM structure is NP-complete. However, some real-world problems could require more wallclock time for calculating the objective value of a single solution, in which case surrogate models are often used.
References
Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD (2010) Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation. J Mach Learn Res 11(Jan):171–234
Bengoetxea E (2002) Inexact graph matching using estimation of distribution algorithms. Ph.D. thesis, University of the Basque Country, Basque Country (2002)
Bengoetxea E, Larrañaga P, Bielza C, Del Pozo JF (2011) Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. J Heuristics 17(5):567–588
Bresler G (2015) Efficiently learning ising models on arbitrary graphs. In: Proceedings of the forty-seventh annual ACM symposium on theory of computing (STOC). ACM, pp 771–782
Casella G, Berger RL (2002) Statistical inference, 2nd edn. Duxbury, Pacific Grove
Cheng Y, Diakonikolas I, Kane D, Stewart A (2018) Robust learning of fixed-structure Bayesian networks. In: NeurIPS, pp 10304–10316
Conover W (1999) Practical nonparametric statistics, 3rd edn. Wiley, New York
Cooper G, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347
El Yafrani M, Martins M, Wagner M, Ahiod B, Delgado M, Lüders R (2018) A hyperheuristic approach based on low-level heuristics for the travelling thief problem. Genet Program Evol Mach 19(1):121–150
El Yafrani M, Martins M, Delgado M, LÃijders R, Sung I, Wagner M, Oliva D (2019) On updating probabilistic graphical models in Bayesian optimisation algorithm. In: 9th Brazilian conference on intelligent systems (BRACIS). Salvador, Brasil, pp 311–316
Etxeberria R, Larrañaga P (1999) Global optimization using Bayesian networks. In: Proceedings of the second symposium on artificial intelligence, CIMAF’99. Editorial Academia, Havana, Cuba, pp 332–339
Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701
Heckerman D, Geiger D, Chickering D (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243
Henrion M (1988) Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Machine intelligence and pattern recognition, vol. 5. Elsevier, pp. 149–163
Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods, vol 751. Wiley, Hoboken
Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, Oxford
Kollat JB, Reed P, Kasprzyk J (2008) A new epsilon-dominance hierarchical Bayesian optimization algorithm for large multiobjective monitoring network design problems. Adv Water Resour 31(5):828–845
Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. The MIT Press, Cambridge
Larrañaga P, Lozano JA (2002) Estimation of distribution algorithms: a new tool for evolutionary computation, vol 2. Springer, Amsterdam
Larrañaga P, Karshenas H, Bielza C, Santana R (2012) A review on probabilistic graphical models in evolutionary computation. J Heuristics 18:795–819
Liaw R, Ting C (2013) Effect of model complexity for estimation of distribution algorithm in nk landscapes. In: 2013 IEEE symposium on foundations of computational intelligence (FOCI), pp 76–83. https://doi.org/10.1109/FOCI.2013.6602458
Liefooghe A, Verel S, Daolio F, Aguirre H, Tanaka K (2015) A feature-based performance analysis in evolutionary multiobjective optimization. In: International conference on evolutionary multi-criterion optimization. Springer, Guimaraes, Portugal, pp 95–109
Martins JP, Delbem AC (2016) Pairwise independence and its impact on estimation of distribution algorithms. Swarm Evol Comput 27:80–96
Martins MSR, El Yafrani M, Delgado MRBS, Wagner M, Ahiod B, Lüders R (2017) HSEDA: a heuristic selection approach based on estimation of distribution algorithm for the travelling thief problem. In: Proceedings of the genetic and evolutionary computation conference, GECCO’17. ACM, New York, NY, USA, pp 361–368
Martins MS, El Yafrani M, Santana R, Delgado MR, Lüders R, Ahiod B (2018) On the performance of multi-objective estimation of distribution algorithms for combinatorial problems. In: IEEE conference on evolutionary computation, CEC’18, pp. 1–8. arXiv:1806.09935
McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2):153–157
Mühlenbein H, Paab G (1996) From recombination of genes to the estimation of distributions I. Binary parameters. Parallel problem solving from nature. PPSN IV: lecture notes in computer science, vol 1411. Springer, London, UK, pp 178–187
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo
Pelikan M (2008) Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, GECCO’08. ACM, Atlanta, pp 1033–1040
Pelikan M, Goldberg DE, Cantú-Paz E (1999) BOA: the Bayesian optimization algorithm. In: Proceedings of the genetic and evolutionary computation conference, GECCO’99, vol I. Morgan Kaufmann Publishers, San Francisco, CA, pp 525–532
Pham N (2011) Investigations of constructive approaches for examination timetabling and 3d-strip packing. Ph.D. thesis, School of Computer Science and Information Technology, University of Nottingham, UK
Santana R, Larrañaga P, Lozano JA (2008) Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. J Heuristics 14:519–547
Santana R, Mendiburu A, Lozano JA (2015) Evolving MNK-landscapes with structural constraints. IEEE Congress on Evolutionary Computation. CEC’15. IEEE, Sendai, Japan, pp 1364–1371
Siegel S, Castellan N (1988) The friedman two-way analysis of variance by ranks. Nonparametric statistics for the behavioral sciences, pp 174–184
Tsamardinos I, Aliferis CF, Statnikov AR, Statnikov E (2003) Algorithms for Large Scale Markov Blanket Discovery. In: FLAIRS conference, vol 2. AAAI Press, St. Augustine, Florida, USA, pp 376–380
Tsamardinos I, Brown LE, Aliferis CF (2006) The max–min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65(1):31–78
Yuan C, Malone B (2013) Learning optimal Bayesian networks: a shortest path perspective. J Artif Intell Res 48(1):23–65
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Scoczynski, M., Delgado, M., Lüders, R. et al. Saving computational budget in Bayesian network-based evolutionary algorithms. Nat Comput 20, 775–790 (2021). https://doi.org/10.1007/s11047-021-09849-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-021-09849-z