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Research topics in discrete estimation of distribution algorithms based on factorizations

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Abstract

In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.

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References

  1. Baluja S (2006) Incorporating a priori knowledge in probabilistic-model based optimization. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 205–222

    Google Scholar 

  2. Bengoetxea E (2003) Inexact Graph matching using estimation of distribution algorithms. Dissertation, Ecole Nationale Supérieure des Télécommunications

  3. Bengoetxea E, Larrañaga P, Bloch I, Perchant A (2002) Estimation of distribution algorithms. a new tool for evolutionary computation chapter solving graph matching with EDAs using permutation-based representation, Kluwer, Boston, pp 239–264

  4. Bilmes J (2000) Dynamic Bayesian multinets. In: Boutilier C, Goldszmidt M (eds) Proceedings of the 16th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers, Menlo Park, pp 38–45

    Google Scholar 

  5. Blickle T, Thiele L (1996) A comparison of selection schemes used in evolutionary algorithms. Evolut Comput 4(4): 361–394

    Article  Google Scholar 

  6. Bosman PA, Grahl J (2008) Matching inductive search bias and problem structure in continuous estimation of distribution algorithms. Eur J Oper Res 185: 1246–1264

    Article  MATH  Google Scholar 

  7. Bosman PA, Thierens D (1999) Linkage information processing in distribution estimation algorithms. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference GECCO-1999, vol I, Orlando, FL, 1999. Morgan Kaufmann Publishers, San Francisco, pp 60–67

  8. Bosman PA, Thierens D (2001) Crossing the road to efficient ideas for permutation problems. In: Spector L, Goodman E, Wu A, Langdon W, Voigt H, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E (eds) Proceedings of the genetic and evolutionary computation conference GECCO-2001, San Francisco, CA, 2001. Morgan Kaufmann Publishers, San Francisco, pp 219–226

  9. Bosman PA, Thierens D (2002) Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms. Int J Approx Reason 31(3): 259–289

    Article  MATH  MathSciNet  Google Scholar 

  10. Bosman PA, Thierens D (2006) Numerical optimization with real-valued estimation-of-distribution algorithms. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable Optimization via Probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 91–120

    Google Scholar 

  11. Branke J (1999) Evolutionary approaches to dynamic optimization problems—a survey. In: Wu AS (ed) Proceedings of the genetic and evolutionary computation conference GECCO-1999, Workshop Program, Orlando, FL, 1999. Morgan Kaufmann Publishers, San Francisco, pp 134–137

  12. Braunstein A, Mézard M, Zecchina R (2005) Survey propagation: An algorithm for satisfiability. Random Struct Algorithms 27(2): 201–226

    Article  MATH  Google Scholar 

  13. Brownlee S, McCall J, Zhang Q, Brown D (2008) Approaches to selection and their effect on fitness modelling in an estimation of distribution algorithm. In: Proceedings of the 2008 congress on evolutionary computation CEC-2008, Hong Kong, 2008. IEEE Press, pp 2626–2633

  14. de la Ossa L, Gámez JA, Puerta JM (2004) Migration of probability models instead of individuals: an alternative when applying the island model to EDAs. In: Parallel problem solving from nature (PPSN VIII), vol 3242. Springer, Berlin, pp 242–252

  15. Echegoyen C, Lozano JA, Santana R, Larrañaga P (2007) Exact Bayesian network learning in estimation of distribution algorithms. In: Proceedings of the 2007 congress on evolutionary computation CEC-2007. IEEE Press, pp 1051–1058

  16. Echegoyen C, Santana R, Lozano JA, Larrañaga P (2008) Linkage in evolutionary computation, chapter The impact of probabilistic learning algorithms in EDAs based on Bayesian networks. Studies in computational intelligence. Springer, Berlin, pp 109–139

  17. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315: 972–976

    Article  MathSciNet  Google Scholar 

  18. Gámez JA, Mateo JL, Puerta JM (2007) EDNA: estimation of dependency networks algorithm. In: Mira J, Álvarez JR (eds) Bio-inspired modeling of cognitive tasks, second international work-conference on the interplay between natural and artificial computation, IWINAC 2007. Lecture notes in computer science, vol 4527. Springer, Berlin, pp 427–436

  19. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  20. Grosset L, Riche R, Haftka RT (2006) A double-distribution statistical algorithm for composite laminate optimization. Struct Multidiscipl Optim 31(1): 49–59

    Article  Google Scholar 

  21. Handa H (2005) Estimation of distribution algorithms with mutation. In: Evolutionary computation in combinatorial optimization. Lecture notes in computer science, vol 3448. Springer, Berlin, pp 112–121

  22. Harik GR, Lobo FG, Sastry K (2006) Linkage learning via probabilistic modeling in the ECGA. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 39–62

    Google Scholar 

  23. Hartmann AK, Weigt M (2005) Phase transitions in combinatorial optimization problems: basics, algorithms and statistical mechanics. Wiley, NY

    Book  MATH  Google Scholar 

  24. Hauschild M, Pelikan M (2008) Enhancing efficiency of hierarchical BOA via distance-based model restrictions. MEDAL Report No. 2008007, Missouri Estimation of Distribution Algorithms Laboratory (MEDAL), April 2008

  25. Hauschild M, Pelikan M, Lima C, Sastry K (2007) Analyzing probabilistic models in hierarchical BOA on traps and spin glasses. In: Thierens D et al (eds) Proceedings of the genetic and evolutionary computation conference GECCO-2007, vol I. London, UK, 2007. ACM Press, New York, pp 523–530

  26. Hauschild M, Pelikan M, Sastry K, Goldberg DE (2008) Using previous models to bias structural learning in the hierarchical BOA. MEDAL Report No. 2008003, Missouri Estimation of Distribution Algorithms Laboratory (MEDAL)

  27. Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20: 197–243

    MATH  Google Scholar 

  28. Henrion M (1988) Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Lemmer JF, Kanal LN (eds) Proceedings of the second annual conference on uncertainty in artificial intelligence. Elsevier, Amsterdam, pp 149–164

    Google Scholar 

  29. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  30. Höns R (2006) Estimation of distribution algorithms and minimum relative entropy. Dissertation, University of Bonn, Bonn, Germany

  31. Höns R, Santana R, Larrañaga P, Lozano JA (2007) Optimization by max-propagation using Kikuchi approximations. Technical Report EHU-KZAA-IK-2/07, Department of Computer Science and Artificial Intelligence, University of the Basque Country, November 2007

  32. Johnson A, Shapiro JL (2002) The importance of selection mechanisms in distribution estimation algorithms. In: Collet P (ed) Proceedings of EA 2001. Lecture notes in computer science, vol 2310. Springer, Berlin, pp 91–103

  33. Jones D (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21: 341–383

    Article  Google Scholar 

  34. Kallel L, Naudts B, Reeves R (2000) Properties of fitness functions and search landscapes. In: Kallel L, Naudts B, Rogers A (eds) Theoretical aspects of evolutionary computing. Springer, Berlin, pp 177–208

    Google Scholar 

  35. Kschischang FR, Frey BJ, Loeliger HA (2001) Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory 47(2): 498–519

    Article  MATH  MathSciNet  Google Scholar 

  36. Larrañaga P (2002) Estimation of distribution algorithms. a new tool for evolutionary computation. A review on estimation of distribution algorithms. Kluwer, Boston, pp 55–98

  37. Larrañaga P, Etxeberria R, Lozano JA, Peña J (2000) Combinatorial optimization by learning and simulation of Bayesian networks. In: Proceedings of the sixteenth annual conference on uncertainty in artificial intelligence (UAI-2000), San Francisco, CA, 2000. Morgan Kaufmann Publishers, Menlo Park, pp 343–352

  38. Larrañaga P, Lozano JA editors (2002) Estimation of distribution algorithms. a new tool for evolutionary computation. Kluwer Academic Publishers, Boston

  39. Lauritzen SL (1996) Graphical models. Clarendon Press, Oxford

    Google Scholar 

  40. Leone M, Weigt M, Weigt M (2007) Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics 23(20): 2708–2715

    Article  Google Scholar 

  41. Lima CF, Pelikan M, Goldberg DE, Lobo FG, Sastry K, Hauschild M (2007) Influence of selection and replacement strategies on linkage learning in BOA. In: Proceedings of the 2007 congress on evolutionary computation CEC-2007. IEEE Press, pp 1083–1090

  42. Lozano JA, Larrañaga P, Inza I, Bengoetxea E, editors (2006) Towards a new evolutionary computation: advances on estimation of distribution algorithms. Springer, Berlin

  43. Lozano JA, Sagarna R, Larrañaga P (2002) Parallel estimation of distribution algorithms. In: Larrañaga P, Lozano JA (eds) Estimation of distribution algorithms. A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, pp 125–142

    Google Scholar 

  44. Madera J, Alba E, Ochoa A (2005) Parallel estimation of distribution algorithms. In: Alba E (eds) Parallel metaheuristics. Wiley, NY, pp 203–222

    Chapter  Google Scholar 

  45. Madera J, Dorronsoro B (2006) Estimation of distribution algorithms. In: Metaheuristic procedures for training neural networks, operations research—computer science interfaces. Springer, Berlin, pp 87–108

  46. Mahnig T, Mühlenbein H (2001) Comparing the adaptive Boltzmann selection schedule SDS to truncation selection. In: Evolutionary computation and probabilistic graphical models. Proceedings of the third symposium on adaptive systems (ISAS-2001), Havana, Cuba, March 2001, pp 121–128

  47. Mahnig T, Mühlenbein H (2001) Optimal mutation rate using Bayesian priors for estimation of distribution algorithms. In: Steinhöfel K (ed) Proceedings of the first symposium on stochastic algorithms: foundations and applications, SAGA-2001. Lecture notes in computer science, vol 2264. Springer, Berlin, pp 33–48

  48. Mendiburu A, Lozano J, Miguel-Alonso J (2005) Parallel implementation of EDAs based on probabilistic graphical models. IEEE Trans Evolut Comput 9(4): 406–423

    Article  Google Scholar 

  49. Mendiburu A, Santana R, Bengoetxea E, Lozano J (2007) A parallel framework for loopy belief propagation. In: Thierens D et al (eds) Proceedings of the genetic and evolutionary computation conference GECCO-2007, vol II, London, UK, 2007, Companion material. ACM Press, New York, pp 2843–2850

  50. Mézard M, Parisi G, Zechina R (2002) Analytic and algorithmic solution of random satisfiability problems. Science 297: 812–812

    Article  Google Scholar 

  51. Michalski RS (2000) Learnable evolution model: Evolutionary processes guided by machine learning. Mach Learn 38: 9–40

    Article  MATH  Google Scholar 

  52. Miquélez T, Bengoetxea E, Larrañaga P (2004) Evolutionary computation based on Bayesian classifiers. Int J Appl Math Comput Sci 14(3): 101–115

    Google Scholar 

  53. Miquélez T, Bengoetxea E, Mendiburu A, Larrañaga P (2007) Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains. Connect Sci 19(4): 297–319

    Article  Google Scholar 

  54. Mühlenbein H (2007) Convergence of estimation of distribution algorithms for finite samples (submitted)

  55. Mühlenbein H, Höns R (2005) The estimation of distributions and the minimum relative entropy principle. Evolut Comput 13(1): 1–27

    Article  Google Scholar 

  56. Mühlenbein H, Höns R (2006) The factorized distributions and the minimum relative entropy principle. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 11–38

    Google Scholar 

  57. Mühlenbein H, Mahnig T (2000) Theoretical Aspects of Evolutionary Computing, chapter Evolutionary algorithms: from recombination to search distributions. Springer, Berlin, pp 137–176

    Google Scholar 

  58. Mühlenbein H, Mahnig T (2001) Evolutionary computation and beyond. In: Uesaka Y, Kanerva P, Asoh H (eds) Foundations of Real-World Intelligence. CSLI Publications, Stanford, pp 123–188

    Google Scholar 

  59. Mühlenbein H, Mahnig T (2002) Evolutionary optimization and the estimation of search distributions with applications to graph bipartitioning. Int J Approx Reason 31(3): 157–192

    Article  MATH  Google Scholar 

  60. Mühlenbein H, Mahnig T, Ochoa A (1999) Schemata, distributions and graphical models in evolutionary optimization. J Heuristics 5(2): 213–247

    Article  Google Scholar 

  61. Mühlenbein H, Paaß G (1996) From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature—PPSN IV. Lectures notes in computer science, Berlin, 1996, vol 1141. Springer, Berlin, pp 178–187

  62. Mühlenbein H, Schlierkamp-Voosen D (1993) Predictive models for the breeder genetic algorithm: I. Continuous parameter optimization. Evolut Comput 1(1): 25–49

    Article  Google Scholar 

  63. Munetomo M, Goldberg DE (1999) A genetic algorithm using the linkage identification by nonlinearity check. In: Proceedings of the IEEE international conference on systems, man, and cybernetics SMC-1999, pp 595–600

  64. Nilsson D (1998) An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Stat Comput 2: 159–173

    Article  Google Scholar 

  65. Ocenasek J, Cantú-Paz E, Pelikan M, Schwarz J (2006) Design of parallel estimation of distribution algorithms. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 187–204

    Google Scholar 

  66. Ocenasek J, Schwarz J (2002) Estimation of distribution algorithm for mixed continuous-discrete optimization problems. In: Proceedings of the 2nd Euro-International symposium on computational intelligence, Kosice, Slovakia, 2002. IOS Press, pp 227–232

  67. Ochoa A (1999) EBBA—Evolutionary best basis algorithm. In: Ochoa A, Soto MR, Santana R (eds) Proceedings of the second international symposium on adaptive systems (ISAS 99), Havana, Cuba, March 1999. Editorial Academia, pp 93–98

  68. Ochoa A, Höns R, Soto MR, Mühlenbein H (2003) A maximum entropy approach to sampling in EDA— the single connected case. In: Progress in pattern recognition, speech and image analysis. Lectures notes in computer science, vol 2905. Springer, Berlin, pp 683–690

  69. Ochoa A, Soto MR (2006) Linking entropy to estimation of distribution algorithms. In: Lozano JA, Larrañaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation: advances on estimation of distribution algorithms. Springer, Berlin, pp 1–38

    Google Scholar 

  70. Ochoa A, Soto MR, Santana R, Madera J, Jorge N (1999) The factorized distribution algorithm and the junction tree: a learning perspective. In: Ochoa A, Soto MR, Santana R (eds) Proceedings of the second symposium on artificial intelligence (CIMAF-99), Havana, Cuba, March 1999, pp 368–377

  71. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo

    Google Scholar 

  72. Pelikan M (2005) Hierarchical Bayesian optimization algorithm. toward a new generation of evolutionary algorithms. Studies in fuzziness and soft computing. Springer, Berlin

    Google Scholar 

  73. Pelikan M, Goldberg DE (2003) Hierarchical BOA solves Ising spin glasses and Max-Sat. IlliGAL Report No. 2003001, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, January 2003

  74. Pelikan M, Goldberg DE, Cantü-Paz E (2000) Bayesian optimization algorithm, population sizing, and time to convergence. In: Proceedings of the genetic and evolutionary computation conference GECCO-2000, pp 275–282

  75. Pelikan M, Hartmann AK (2006) 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, studies in computational intelligence. Springer, Berlin, pp 333–349

    Google Scholar 

  76. Pelikan M, Sastry K (2004) Fitness inheritance in the Bayesian optimization algorithm. In: Genetic and evolutionary computation—GECCO 2004. Lectures notes in computer science, vol 3103. Springer, Berlin, pp 48–59

  77. Pelikan M, Sastry K, Cantú-Paz E (eds) (2006) Scalable optimization via probabilistic modeling: from algorithms to applications. Studies in computational intelligence. Springer, Berlin

  78. Pelikan M, Sastry K, Goldberg DE (2008) Sporadic model building for efficiency enhancement of the hierarchical BOA. Genet Progr Evol Mach 9(1): 53–84

    Article  Google Scholar 

  79. Peña J, Lozano JA, Larrañaga P (2005) Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks. Evolut Comput 13(1): 43–66

    Article  Google Scholar 

  80. Peña JM, Robles V, Larrañaga P, Herves V, Rosales F, Pérez MS (2004) GA-EDA: Hybrid evolutionary algorithm using genetic and estimation of distribution algorithms. In: Proceedings of the 17th international conference on innovations in applied artificial intelligence. Lecture notes in artificial intelligence, vol 3029. Springer, Berlin, pp 361–371

  81. Pereira FB, Machado P, Costa E, Cardoso A, Ochoa A, Santana R, Soto MR (2000) Too busy to learn. In: Proceedings of the 2000 congress on evolutionary computation CEC-2000, La Jolla Marriott Hotel La Jolla, California, USA, July 2000. IEEE Press, pp 720–727

  82. Prügel-Bennet A (2004) Symmetry breaking in population-based optimization. IEEE Trans Evolut Comput 8(1): 63–79

    Article  Google Scholar 

  83. Rivera JP, Santana R (2000) Design of an algorithm based on the estimation of distributions to generate new rules in the XCS classifier system. Technical Report ICIMAF 2000-100, CEMAFIT 2000-78, Institute of Cybernetics, Mathematics and Physics, Havana, Cuba, June 2000

  84. Robles V, de Miguel P, Larrañaga P (2002) Solving the traveling salesman problem with EDAs. In: Larrañaga P, Lozano JA (eds) Estimation of distribution algorithms. A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, pp 227–238

    Google Scholar 

  85. Robles V, Peña JM, Pérez MS, Herves V (2006) GA-EDA: A new hybrid cooperative search evolutionary algorithm. In: Lozano JA, Larrañaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, Berlin, pp 187–220

    Google Scholar 

  86. Romero T, Larrañaga P (2008) Triangulation of Bayesian networks with recursive estimation of distribution algorithms. Int J Approx Reason (accepted for publication)

  87. Romero T, Larrañaga P, Sierra B (2004) Learning Bayesian networks in the space of orderings with estimation of distribution algorithms. Int J Pattern Recognit Artif Intell 18(4): 607–625

    Article  Google Scholar 

  88. Rosete A, Ochoa A, Sebag M (1999) Efficient-discarding fitness functions. In: Wu AS (ed) Proceedings of the genetic and evolutionary computation conference GECCO-1999, late breaking papers, Orlando, FL, 1999. Morgan Kaufmann Publishers, San Francisco, pp 223–228

  89. Sagarna R, Lozano JA (2006) Scatter search in software testing, comparison and collaboration with estimation of distribution algorithms. Eur J Oper Res 169: 392–412

    Article  MATH  MathSciNet  Google Scholar 

  90. Santana R (2003) Factorized distribution algorithms: selection without selected population. In Proceedings of the 17th European simulation multiconference ESM-2003, Nottingham, England, pp 91–97

  91. Santana R (2005) Estimation of distribution algorithms with Kikuchi approximations. Evolut Comput 13(1): 67–97

    Article  Google Scholar 

  92. Santana R (2006) Advances in probabilistic graphical models for optimization and learning. applications in protein modelling. Dissertation, University, Basque Country

  93. Santana R, de León EP, Ochoa A (1999) The edge incident model. In: Ochoa A, Soto MR, Santana R (eds) Proceedings of the second symposium on artificial intelligence (CIMAF-99), Havana, Cuba, March 1999, pp 352–359

  94. Santana R, Larrañaga P, Lozano JA (2005) Interactions and dependencies in estimation of distribution algorithms. In: Proceedings of the 2005 congress on evolutionary computation CEC-2005, Edinburgh, U.K., 2005. IEEE Press, pp 1418–1425

  95. Santana R, Larrañaga P, Lozano JA (2006) Mixtures of Kikuchi approximations. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) Proceedings of the 17th European conference on machine learning: ECML 2006. Lecture notes in artificial intelligence, vol 4212. Springer, Berlin, pp 365–376

  96. Santana R, Larrañaga P, Lozano JA (2007) The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms. In: Marchiori E, Moore JH, Rajapakse JC (eds) Proceedings of the fifth european conference on evolutionary computation, machine learning and data mining in bioinformatics. Lecture notes in computer science, vol 4447. Springer, Berlin, pp 247–257

  97. Santana R, Larrañaga P, Lozano JA (2007) Side chain placement using estimation of distribution algorithms. Artif Intell Med 39(1): 49–63

    Article  Google Scholar 

  98. Santana R, Larrañaga P, Lozano JA (2008) Adaptive estimation of distribution algorithms. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and multilevel metaheuristics. Studies in computational intelligence, vol 136. Springer, Berlin, pp 177–197

  99. 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

    Article  MATH  Google Scholar 

  100. Santana R, Larrañaga P, Lozano JA (2008) Protein folding in simplified models with estimation of distribution algorithms. IEEE Trans Evolut Comput 12(4): 418–438

    Article  Google Scholar 

  101. Santana R, Ochoa A, Soto MR (2001) Factorized Distribution Algorithms for functions with unitation constraints. In: Evolutionary Computation and probabilistic graphical models. Proceedings of the third symposium on adaptive systems (ISAS-2001), Havana, Cuba, March 2001, pp 158–165

  102. Sastry K, Goldberg DE, Llorá X (2007) Towards billion-bit optimization via a parallel estimation of distribution algorithm. In: Thierens D et al. (ed.), Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2007, vol I. London, UK, 2007. ACM Press, New York, pp 577–584

  103. Sastry K, Pelikan M, Goldberg D (2004) Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation. In: Proceedings of the 2004 congress on evolutionary computation CEC-2004, Portland, Oregon, 2004. IEEE Press, pp 720–727

  104. Sastry K, Pelikan M, Goldberg DE (2006) Efficiency enhancement of estimation of distribution algorithms. In: PelikanM Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 161–186

    Google Scholar 

  105. Shakya S, McCall J (2007) Optimization by estimation of distribution with DEUM framework based on Markov random fields. Int J Autom Comput 4(3): 262–272

    Article  Google Scholar 

  106. Shakya S, McCall J, Brown D (2005) Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis. In: Beyer H-G, O’Reilly U-M (eds) Proceedings of genetic and evolutionary computation conference GECCO-2005, Washington, D.C., USA, 2005. ACM Press, New York, pp 727–734

  107. Shan Y, McKay RI, Essam D, Abbass HA (2006) A survey of probabilistic model building genetic programming. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, studies in computational intelligence. Springer, Berlin, pp 121–160

    Google Scholar 

  108. Shapiro JL (2005) Drift and scaling in estimation of distribution algorithms. Evolut Comput 13(1): 99–123

    Article  Google Scholar 

  109. Soto MR (2003) A Single Connected factorized distribution algorithm and its cost of evaluation. Dissertation, University of Havana, Havana, Cuba, July 2003 (in Spanish)

  110. Soto MR, Ochoa A (2000) A factorized distribution algorithm based on polytrees. In: Proceedings of the 2000 congress on evolutionary computation CEC-2000, La Jolla Marriott Hotel La Jolla, California, USA, 6–9 July 2000. IEEE Press, pp 232–237

  111. Soto MR, Ochoa A, Acid S, Campos LM (1999) Bayesian evolutionary algorithms based on simplified models. In: Ochoa A, Soto MR, Santana R (eds) Proceedings of the second symposium on artificial intelligence (CIMAF-99), Havana, Cuba, March 1999, pp 360–367

  112. Suwannik W, Chongstitvatana P (2008) Solving one-billion-bit noisy OneMax problem using estimation distribution algorithm with arithmetic coding. In: Proceedings of the 2008 congress on evolutionary computation CEC-2008, Hong Kong, 2008. IEEE Press, pp 1203–1206

  113. Thierens D, Bosman PA (2001) Multi-objective mixture-based iterated density estimation evolutionary algorithms. In: Spector L, Goodman E, Wu A, Langdon W, Voigt H, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E (eds) Proceedings of the genetic and evolutionary computation conference GECCO-2001, San Francisco, CA, 2001. Morgan Kaufmann Publishers, Menlo Park, pp 663–670

  114. Tsuji M, Munetomo M, Akama K (2006) Linkage identification by fitness difference clustering. Evolut Comput 14(4): 383–409

    Article  Google Scholar 

  115. Tsutsui S (2003) Edge histogram based sampling and its application to ACO. In: Proceedings of the frontiers of evolutionary algorithm conference FEA-2003, pp 283–286

  116. Wainwright MJ, Jaakkola TS, Willsky AS (2005) MAP estimation via agreement on (hyper)trees: Message-passing and linear programming. IEEE Trans Inf Theory 51(11): 3697–3717

    Article  MathSciNet  Google Scholar 

  117. Wang J (2007) Genetic particle swarm optimization based on estimation of distribution. In: Li K et al (eds) Proceedings LSMS-2007. Lecture notes in computer science, vol 4688. Springer, Berlin, pp 287–296

  118. Wang X, Wang H (2004) Evolutionary optimization with Markov random field prior. IEEE Trans Evolut Comput 8(6): 567–579

    Article  Google Scholar 

  119. Whittaker J (1991) Graphical models in applied multivariate statistics. Wiley Series in Probability and Mathematical Statistics, New York

    Google Scholar 

  120. William WH, Krasnogor N, Smith JE (eds) (2004) Recent advances in memetic algorithms. Studies in fuzziness and soft computing, vol 166. Springer, Berlin

  121. Wright AH, Pulavarty S (2005) Estimation of distribution algorithm based on linkage discovery and factorization. In: Beyer H-G, OReilly U-M (eds) Proceedings of genetic and evolutionary computation conference GECCO-2005, Washington, DC, USA, 2005. ACM, New York, pp 695–703

  122. Yanover C, Weiss Y (2003) Approximate inference and protein-folding. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems, vol 15. MIT Press, Cambridge, pp 1457–1464

    Google Scholar 

  123. Yanover C, Weiss Y (2004) Finding the M most probable configurations using loopy belief propagation. In: Thrun S, Saul L, Schölkopf B editors (2004) Advances in neural information processing systems, vol 16. MIT Press, Cambridge

  124. Yedidia JS, Freeman WT, Weiss Y (2005) Constructing free energy approximations and generalized belief propagation algorithms. IEEE Trans Inf Theory 51(7): 2282–2312

    Article  MathSciNet  Google Scholar 

  125. Yu T-L, Sastry K, Goldberg DE, Pelikan M (2007) Population sizing for entropy-based model building in genetic algorithms. In: Thierens D et al (ed) Proceedings of the genetic and evolutionary computation conference GECCO-2007, vol I. London, UK, 2007. ACM Press, New York, pp 601–608

  126. Zhang Q, Li H (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6): 712–731

    Article  Google Scholar 

  127. Zhang Q (2004) On the convergence of a factorized distribution algorithm with truncation selection. Complexity 9(4): 17–23

    Article  MathSciNet  Google Scholar 

  128. Zhang Q, Mühlenbein H (2004) On the convergence of a class of estimation of distribution algorithms. IEEE Trans Evolut Comput 8(2): 127–136

    Article  Google Scholar 

  129. Zhang Q, Sun J, Tsang EPK (2005) Evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans Evolut Comput 9(2): 192–200

    Article  Google Scholar 

  130. Zhang Q, Sun J, Tsang EPK, Ford JA (2003) Hybrid estimation of distribution algorithm for global optimization. Eng Comput 21(1): 91–107

    Google Scholar 

  131. Zhang Q, Zhou A, Jin A (2008) Modelling the regularity in estimation of distribution algorithm for continuous multi-objective evolutionary optimization with variable linkages. IEEE Trans Evolut Comput 12(1): 49–63

    Article  Google Scholar 

  132. Zhou S, Heckendorn RB, Sun Z (2007) Detecting the epistatic structure of generalized embedded landscape (submitted)

  133. Zhou S, Sun Z, Heckendorn RB (2007) Extended probe method for linkage discovery over high-cardinality alphabets. In: Thierens D et al (eds) Proceedings of the genetic and evolutionary computation conference GECCO-2007, vol I. New York, NY, USA, 2007. ACM Press, New York, pp 1484–1491

  134. Zlochin M, Birattari M, Meuleau N, Dorigo M (2004) Model-based search for combinatorial optimization: a critical survey. Ann Oper Res 131(1–4): 373–395

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian-Donostia, Spain

    Roberto Santana & Jose A. Lozano

  2. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Madrid, Spain

    Pedro Larrañaga

Authors
  1. Roberto Santana
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  2. Pedro Larrañaga
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  3. Jose A. Lozano
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Correspondence to Roberto Santana.

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Santana, R., Larrañaga, P. & Lozano, J.A. Research topics in discrete estimation of distribution algorithms based on factorizations. Memetic Comp. 1, 35–54 (2009). https://doi.org/10.1007/s12293-008-0002-7

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  • DOI: https://doi.org/10.1007/s12293-008-0002-7

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