A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

Progress in Artificial Intelligence

Volumn 1, Issue 1, 2012, Pages 103-117

  Ceberio, Josu ^a   Irurozki, Ekhine ^a   Mendiburu, Alexander ^a   Lozano, Jose A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Estimation of distribution algorithms; Evolutionary computation; Permutation based optimization problems; Probabilistic permutation modelling

Indexed keywords

COMBINATORIAL OPTIMIZATION; INTEGER PROGRAMMING; PROBABILITY DISTRIBUTIONS;

COMBINATORIAL OPTIMIZATION PROBLEMS; ESTIMATION OF DISTRIBUTION ALGORITHMS; OPTIMIZATION PROBLEMS; PROBABILISTIC MODELING; PROBABILISTIC MODELS; REAL-VALUE PROBLEMS; REAL-WORLD OPTIMIZATION; STATE OF THE ART;

EVOLUTIONARY ALGORITHMS;

EID: 84855781728     PISSN: 21926352     EISSN: 21926360     Source Type: Journal    
DOI: 10.1007/s13748-011-0005-3     Document Type: Review

References (56)

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)
2. Bean, C.J.: Genetic algorithms and random keys for sequencing and optimization. INFORMS J. Comput. 6(2), 154-160 (1994)
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)
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)
5. Bosman, P.A.N., Thierens, D.: Crossing the road to efficient IDEAs for permutation problems. In: Spector, L., et al. (eds.) Genetic and Evolutionary Computation Conference, GECCO 2001, Proceedings, San Francisco, California, USA, 2001, pp. 219-226. Morgan Kaufmann, Massachusetts (2001)
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)
7. Chen, S., Chen, M.: Bi-variate artificial chromosomes with genetic algorithm for single machine scheduling problems with sequencedependent setup times. In: Proceedings of the Congress on Evolutionary Computation (2011)
8. Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 14(3), 462-467 (1968)
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)
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)
11. Fligner, A.M., Verducci, S.J.: Distance based ranking Models. J. Royal Stat. Soc. 48(3), 359-369 (1986)
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)
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)
14. Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison/Wesley, Reading (1989)
15. Goldberg, D.E., Lingle Jr., R.: Alleles Loci and the traveling salesman problem. In: ICGA, pp. 154-159 (1985)
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)
17. Gupta, J., Stafford, E.J.: Flow shop scheduling research after five decades. Eur. J. Oper. Res. 169, 699-711 (2006)
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)
19. Hunter, R.D.: MM Algorithms for generalized Bradley-Terry models. Ann. Stat. 32(1), 384-406 (2004)
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)
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)
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)
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)
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)
26. Larrañaga, P., Lozano, J.A.: Estimation of distribution algorithms: a new tool for evolutionary computation. Kluwer, Dordrecht (2002)
27. Lebanon, G., Mao, Y.: Non-Parametric modeling of partially ranked data. J. Mach. Learn. Res. (JMLR) 9, 2401-2429 (2008)
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)
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)
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. Kluwer, Dordrecht (2002)
31. Luce, R.D.: Individual Choice Behavior. Wiley, New York (1959)
32. Mallows, L.C.: Non-null ranking models. Biometrika 44(1-2), 114-130 (1957)
33. Mandhani, B., Meila, M.: Tractable search for learning exponential models of rankings. In: Artificial Intelligence and Statistics (AISTATS), April (2009)
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)
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)
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)
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)
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)
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)
40. Pelikan, M., Tsutsui, S., Kalapala, R.: Dependency trees, permutations, and quadratic assignment problem. Technical report, Medal Report No. 2007003 (2007)
41. Plackett, R.L.: The analysis of permutations. J. Royal Stat. Soc. 24(10), 193-202 (1975)
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)
43. Romero, T., Larrañaga, P.: Triangulation of Bayesian networks with recursive estimation of distribution algorithms. Int. J. Approx. Reason. 50(3), 472-484 (2009)
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)
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)
46. Tsutsui, S.: Probabilistic model-building genetic algorithms in permutation representation domain using edge histogram. In: PPSN, pp. 224-233 (2002)
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)
48. Tsutsui, S.: Effect of using partial solutions in edge histogram sampling algorithms with different local searches. In: SMC, pp. 2137-2142 (2009)
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)
50. Tsutsui, S., Pelikan, M., Goldberg, D.E.: Using edge histogram models to solve permutation problems with probabilistic modelbuilding genetic algorithms. Technical report, IlliGAL Report No. 2003022 (2003)
51. Tsutsui, S., Pelikan, M., Goldberg, D.E.: Node histogram vs. edge histogram: a comparison of PMBGAs in permutation domains. Technical report, Medal (2006)
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)
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)
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)
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)
56. Zhigljavsky, A.A.: Theory of Global Random Search. Kluwer, Dordrecht (1991)