A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems
- 1.8k Downloads
- 46 Citations
Abstract
Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.
Keywords
Evolutionary computation Estimation of distribution algorithms Permutation-based optimization problems Probabilistic permutation modellingReferences
- 1.Agrawal, S., Wang, Z., Ye, Y.: Parimutuel betting on permutations. In: Internet and Network Economics. Lecture Notes in Computer Science, vol. 5385, pp. 126–137. Springer, Berlin (2008)Google Scholar
- 2.Bean C.J.: Genetic algorithms and random keys for sequencing and optimization. INFORMS J. Comput. 6(2), 154–160 (1994)MATHCrossRefGoogle Scholar
- 3.Bengoetxea E., Larrañaga P., Bloch I., Perchant A., Boeres C.: Inexact graph matching by means of estimation of distribution algorithms. Pattern Recognit. 35(12), 2867–2880 (2002)MATHCrossRefGoogle Scholar
- 4.Bosman P.A.N., Thierens D.: Expanding from discrete to continuous estimation of distribution algorithms: the IDEA. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Guervós, J.J.M., Schwefel, H.P (eds) PPSN. Lecture Notes in Computer Science vol. 1917., pp. 767–776. Springer, Berlin (2000)Google Scholar
- 5.Bosman P.A.N., Thierens D. et al.: Crossing the road to efficient IDEAs for permutation problems. In: Spector, L. (eds) Genetic and Evolutionary Computation Conference, GECCO 2001, Proceedings, San Francisco, California, USA, 2001., pp. 219–226. Morgan Kaufmann, Massachusetts (2001)Google Scholar
- 6.Brownlee A.E.I., Pelikan M., McCall J.A.W., Petrovski A.: An application of a multivariate estimation of distribution algorithm to cancer chemotherapy. In: Ryan, C., Keijzer, M. (eds) GECCO., pp. 463–464. ACM, New York (2008)CrossRefGoogle Scholar
- 7.Chen, S., Chen, M.: Bi-variate artificial chromosomes with genetic algorithm for single machine scheduling problems with sequence-dependent setup times. In: Proceedings of the Congress on Evolutionary Computation (2011)Google Scholar
- 8.Chow C., Liu C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 14(3), 462–467 (1968)MathSciNetMATHCrossRefGoogle Scholar
- 9.Cohen, W.W., Schapire, R. E., Singer, Y.: Learning to order things. In: Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems 10, NIPS ’97, pp. 451–457. MIT Press, Cambridge (1998)Google Scholar
- 10.De Bonet J.S., Isbell C.L., Viola P.: MIMIC: Finding optima by estimating probability densities. In: Mozer, M., Jordan, M., Petsche, Th (eds) Advances in Neural Information Processing Systems vol 9., MIT Press, Cambridge (1997)Google Scholar
- 11.Fligner A.M., Verducci S.J. Verducci: Distance based ranking Models. J. Royal Stat. Soc. 48(3), 359–369 (1986)MathSciNetMATHGoogle Scholar
- 12.Garcia S., Herrera F.: An extension on “Statistical Comparisons of Classifiers over Multiple Data Set” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)MATHGoogle Scholar
- 13.Garcia S., Molina D., Lozano M., Herrera F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15(6), 617–644 (2009)MATHCrossRefGoogle Scholar
- 14.Goldberg D.E.: Genetic algorithms in search optimization and machine learning. Addison/Wesley, Reading (1989)MATHGoogle Scholar
- 15.Goldberg, D.E., Lingle Jr., R.: Alleles Loci and the traveling salesman problem. In: ICGA, pp. 154–159 (1985)Google Scholar
- 16.Guiver, J., Snelson, E.: Bayesian inference for Plackett-Luce ranking models. In: International Conference on Machine Learning (ICML 2009), ICML’09, pp. 377–384. ACM, New York (2009)Google Scholar
- 17.Gupta J., Stafford E.J. Stafford: Flow shop scheduling research after five decades. Eur. J. Oper. Res. 169, 699–711 (2006)MATHCrossRefGoogle Scholar
- 18.Henrion M.: Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Lemmer, J.F., Kanal, L.N. (eds) UAI., pp. 149–164. Elsevier, Amsterdam (1986)Google Scholar
- 19.Hunter R.D. Hunter: MM Algorithms for generalized Bradley–Terry models. Ann. Stat. 32(1), 384–406 (2004)MathSciNetMATHCrossRefGoogle Scholar
- 20.Jarboui B., Eddaly M., Siarry P.: An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems. Comput. OR 36(9), 2638–2646 (2009)MathSciNetMATHCrossRefGoogle Scholar
- 21.Jiang S., Ziver A., Carter J., Pain C., Goddard A., Franklin S., Phillips H.: Estimation of distribution algorithms for nuclear reactor fuel management optimisation. Ann. Nuclear Energy 33(11–12), 1039–1057 (2006)CrossRefGoogle Scholar
- 22.Knjazew, D., Goldberg, D.E.: Omega—ordering messy ga: solving permutation problems with the fast genetic algorithm and random keys. In: GECCO, pp. 181–188 (2000)Google Scholar
- 23.Koopmans, T.C., Beckmann, M.J.: Assignment problems and the location of economic activities. Cowles Foundation Discussion Papers 4, Cowles Foundation for Research in Economics, Yale University. http://ideas.repec.org/p/cwl/cwldpp/4.html (1955)
- 24.Larrañaga, P., Etxeberria, R., Lozano, J.A., Peña, J.M.: Combinatorial optimization by learning and simulation of Bayesian networks. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence, UAI 2000, pp. 343–352, Stanford (2000)Google Scholar
- 25.Larrañaga, P., Etxeberria, R., Lozano, J.A., Peña J.M.: Optimization in continuous domains by learning and simulation of Gaussian networks. In: Proceedings of the Workshop in Optimization by Building and using Probabilistic Models. A Workshop within the 2000 Genetic and Evolutionary Computation Conference, GECCO 2000, pp. 201–204, Las Vegas (2000)Google Scholar
- 26.Larrañaga P., Lozano J.A.: Estimation of distribution algorithms a new tool for evolutionary computation. Kluwer, Dordrecht (2002)MATHGoogle Scholar
- 27.Lebanon G., Mao Y.: Non-Parametric modeling of partially ranked data. J. Mach. Learn. Res. (JMLR) 9, 2401–2429 (2008)MathSciNetMATHGoogle Scholar
- 28.Liu H., Gao L., Pan Q.: A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Expert Syst. Appl. 38, 4348–4360 (2011)CrossRefGoogle Scholar
- 29.Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E.: Towards a new evolutionary computation: advances on estimation of distribution algorithms (Studies in Fuzziness and Soft Computing). Springer, New York (2006)Google Scholar
- 30.Lozano J.A., Mendiburu A.: Estimation of Distribution Algorithms applied to the job schedulling problem. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation., pp. 1–2. Kluwer, Dordrecht (2002)Google Scholar
- 31.Luce R.D.: Individual Choice Behavior. Wiley, New York (1959)MATHGoogle Scholar
- 32.Mallows L.C. Mallows: Non-null ranking models. Biometrika 44(1–2), 114–130 (1957)MathSciNetMATHGoogle Scholar
- 33.Mandhani, B., Meila, M.: Tractable search for learning exponential models of rankings. In: Artificial Intelligence and Statistics (AISTATS), April (2009)Google Scholar
- 34.Mendiburu A., Lozano J.A., Miguel-Alonso J.: Parallel implementation of EDAs based on probabilistic graphical models. IEEE Trans. Evol. Comput. 9(4), 406–423 (2005)CrossRefGoogle Scholar
- 35.Mendiburu A., Miguel-Alonso J., Lozano J.A., Ostra M., Ubide C.: Parallel EDAs to create multivariate calibration models for quantitative chemical applications. J. Parallel Distrib. Comput. 66(8), 1002–1013 (2006)CrossRefGoogle Scholar
- 36.Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Lecture Notes in Computer Science 1411: Parallel Problem Solving from Nature—PPSN IV, pp. 178–187 (1996)Google Scholar
- 37.Pelikan, M., Goldberg, D.E.: Hierarchical problem solving and the Bayesian optimization algorithm. In: Whitley, D., Goldberg, D.E., Cantú-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, vol. 1, pp. 267–274. Morgan Kaufmann Publishers, Menlo Park (2000)Google Scholar
- 38.Pelikan M., Goldberg D.E., Lobo F.G.: A survey of optimization by building and using probabilistic models. Comput. Optim. Appl. 21(1), 5–20 (2002)MathSciNetMATHCrossRefGoogle Scholar
- 39.Pelikan, M., Sastry, K., Cantú-Paz, E.: Scalable optimization via probabilistic modeling: from algorithms to applications (Studies in Computational Intelligence). Springer, New York (2006)Google Scholar
- 40.Pelikan, M., Tsutsui, S., Kalapala, R.: Dependency trees, permutations, and quadratic assignment problem. Technical report, Medal Report No. 2007003 (2007)Google Scholar
- 41.Plackett R.L.: The analysis of permutations. J. Royal Stat. Soc. 24(10), 193–202 (1975)MathSciNetGoogle Scholar
- 42.Robles V., de Miguel P., Larrañaga P.: Solving the traveling salesman problem with EDAs. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of distribution algorithms a new tool for evolutionary computation., Kluwer, Dordrecht (2002)Google Scholar
- 43.Romero T., Larrañaga P.: Triangulation of Bayesian networks with recursive estimation of distribution algorithms. Int. J. Approx. Reason. 50(3), 472–484 (2009)CrossRefGoogle Scholar
- 44.Sagarna R., Lozano J.A.: Scatter Search in software testing, comparison and collaboration with estimation of distribution algorithms. Eur. J. Oper. Res. 169(2), 392–412 (2006)MathSciNetMATHCrossRefGoogle Scholar
- 45.Santana R., Larrañaga P., Lozano J.A.: Protein folding in simplified models with estimation of distribution algorithms. IEEE Trans. Evol. Comput. 12(4), 418–438 (2008)CrossRefGoogle Scholar
- 46.Tsutsui, S.: Probabilistic model-building genetic algorithms in permutation representation domain using edge histogram. In: PPSN, pp. 224–233 (2002)Google Scholar
- 47.Tsutsui, S.: A comparative study of sampling methods in node histogram models with probabilistic model-building genetic algorithms. In: IEEE International Conference on Systems, Man, and Cybernetics. 8–11 October 2006, Taipei, vol. 4, pp. 3132–3137 (2006)Google Scholar
- 48.Tsutsui, S.: Effect of using partial solutions in edge histogram sampling algorithms with different local searches. In: SMC, pp. 2137–2142 (2009)Google Scholar
- 49.Tsutsui, S., Miki, M.: Solving flow shop scheduling problems with probabilistic model-building genetic algorithms using edge histograms. In: 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 02), pp. 776–780 (2002)Google Scholar
- 50.Tsutsui, S., Pelikan, M., Goldberg, D.E.: Using edge histogram models to solve permutation problems with probabilistic model-building genetic algorithms. Technical report, IlliGAL Report No. 2003022 (2003)Google Scholar
- 51.Tsutsui, S., Pelikan, M., Goldberg, D.E.: Node histogram vs. edge histogram: a comparison of PMBGAs in permutation domains. Technical report, Medal (2006)Google Scholar
- 52.Tsutsui, S., Wilson, G.: Solving capacitated vehicle routing problems using edge histogram based sampling algorithms. In: Proceedings of the IEEE Conference on Evolutionary Computation, Portland, Oregon (USA), pp. 1150–1157 (2004)Google Scholar
- 53.Yuan, B., Orlowska, M.E., Sadiq, S.W.: Finding the optimal path in 3d spaces using EDAs—the wireless sensor networks scenario. In: ICANNGA (1), pp. 536–545 (2007)Google Scholar
- 54.Zhang, Q., Sun, J., Tsang, E., Ford, J.: Combination of guided local search and estimation of distribution algorithm for solving quadratic assignment problem. In: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pp. 42–48 (2004)Google Scholar
- 55.Zhang, Q., Sun, J., Tsang, E., Ford, J.: Estimation of distribution algorithm with 2-opt local search for the quadratic assignment problem. Stud. Fuzziness Soft Comput. 192/2006:281–292 (2006)Google Scholar
- 56.Zhigljavsky A.A.: Theory of Global Random Search. Kluwer, Dordrecht (1991)Google Scholar