A Bayesian network approach to program generation

IEEE Transactions on Evolutionary Computation

Volumn 12, Issue 6, 2008, Pages 750-764

  Hasegawa, Yoshihiko ^a   Iba, Hitoshi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Bayesian network; Estimation of distribution algorithm; Genetic programming (GP)

Indexed keywords

COMPUTER PROGRAMMING; DISTRIBUTED PARAMETER NETWORKS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; INFERENCE ENGINES; INTELLIGENT NETWORKS; PROBABILITY; PROBABILITY DISTRIBUTIONS; SPEECH ANALYSIS;

BUILDING BLOCKS; COMPUTATIONAL EXPERIMENTS; CONDITIONAL PROBABILITY TABLES; CROSS OVERS; CROSSOVER POINTS; DECEPTIVE PROBLEMS; ESTIMATION OF DISTRIBUTION ALGORITHM; GENETIC OPERATORS; MEMORY RESOURCES; NEW APPROACHES; OPTIMIZATION ALGORITHMS; PARSE TREES; PROBABILISTIC TECHNIQUES; PROGRAM EVOLUTIONS; PROGRAM GENERATIONS;

BAYESIAN NETWORKS;

EID: 57149131835     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2008.915999     Document Type: Article

References (36)

1. S. Baluja, "Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning," Carnegie Mellon Univ., Pittsburgh, PA, Tech. Rep. CMU-CS-94-163, 1994.
2. P. A. N. Bosman and E. D. de Jong, "Grammar transformations in an EDA for genetic programming," Inst. Information and Computing Sciences, Utrecht Univ., Utrecht, The Netherlands, Tech. Rep. UU-CS-2004-047, 2004.
3. G. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Mach. Learn., vol. 9, pp. 309-347, 1992.
4. R. Etxeberria and P. Larrañaga, "Global optimization using Bayesian networks," in Proc. 2nd Symp. Artif. Intell. (CIMAF-99 , 1999, pp. 332-339.
5. N. Friedman and Z. Yakhini, "On the sample complexity of learning Bayesian networks," in Proc. 12th Conf. Uncertainty in Artif. Intell., 1996, pp. 274-282.
6. C. Gathercole and P. Ross, "An adverse interaction between crossover and restricted tree depth in genetic programming," in Proc. 1st Annu. Conf. Genetic Programming, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, Eds., 1996, pp. 291-296.
7. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Mach. Learn.. Reading, MA: Addison-Wesley, 1989.
8. G. Harik, "Linkage learning via probabilistic modeling in the ECGA," IlliGAL Rep. (99010), 1999.
9. Y. Hasegawa and H. Iba, "Estimation of Bayesian network for program generation," in Proc. 3rd Asian-Pacific Workshop on Genetic Programming, Hanoi, Vietnam, 2006a, pp. 35-46.
10. Y. Hasegawa and H. Iba, "Optimizing programs with estimation of Bayesian network," in Proc. 2006 World Congr. Comput. Intell., Vancouver, BC, Canada, 2006b, pp. 5527-5534.
11. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press, 1975.
12. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
13. J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press, 1994.
14. W. B. Langdon and R. Poli, "An analysis of the MAX problem in genetic programming," in Proc. 2nd Annu. Conf. Genetic Programming, J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, Eds., 1997, pp. 222-230.
15. P. Larrañaga and J. A. Lozano, Estimation of Distribution Algorithms. Norwell, MA: Kluwer, 2002.
16. M. Looks, "Learning Computer programs with the Bayesian optimization algorithm," Master thesis, Washington Univ., Sever Inst. Technol., St. Louis, MO, 2005.
17. M. Looks, B. Goertzel, and C. Pennachin, "Learning computer programs with the Bayesian optimization algorithm," in Proc. 2005 Conf. Genetic Evol. Comput., GECCO 2005, H. G. Beyer et al., Ed., Washington, DC, 2005, vol. 2, pp. 747-748.
18. M. Mitchell, S. Forrest, and J. H. Holland, "The royal road for genetic algorithms: Fitness landscapes and GA performance," in Proc. 1st Euro. Conf. Artif. Life, 1991 Towards a Practice of Autonomous Syst., F. J. Varela and P. Bourgine, Eds., 1992, pp. 245-254.
19. H. Mühlenbein and T. Mahnig, "FDA - A scalable evolutionary algorithm for the optimization of additively decomposed functions," Evol. Comput., vol. 7, no. 4, pp. 353-376, 1999.
20. R. E. Neapolitan, Learning Bayesian Networks. Upper Saddle River, NJ: Pearson Education, 2004.
21. M. Pelikan, "Bayesian optimization algorithm: From single level to hierarchy," Ph.D. dissertation, Univ. Illinois at Urbana-Champaign, Urbana, IL, 2002, also IlliGAL Rep. No. 2002023.
22. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz, "BOA: The Bayesian optimization algorithm," in Proc. Genetic Evol. Comput. Conf. GECCO 1999, W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, Eds., Orlando, FL, 1999, vol. I, pp. 525-532.
23. J. M. Peña, J. A. Lozano, and P. Larrañaga, "Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks," Evol. Comput., vol. 13, no. 1, pp. 43-66, 2005.
24. W. F. Punch, "How effective are multiple populations in genetic programming," in Proc. 3rd Annu. Conf. Genetic Programming 1998:, J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. Riolo, Eds., 1998, pp. 308-313.
25. C. P. Quesenberry and D. C. Hurst, "Large sample simultaneous confidence intervals for multinomial proportions," Technometrics, vol. 6, pp. 191-195, 1964.
26. A. Ratle and M. Sebag, "Avoiding the bloat with probabilistic grammar-guided genetic programming," in Proc. 5th Int. Conf. Artif. Evol. 2001, P. Collet, C. Fonlupt, J.-K. Hao, E. Lutton, and M. Schoenauer, Eds., Creusot, France, 2001, vol. 2310, Lecture Notes in Computer Science, pp. 255-266.
27. E. N. Regolin and A. T. R. Pozo, "Bayesian automatic programming," in Proc. 8th Eur. Conf. Genetic Programming, M. Keijzer, A. Tettamanzi, P. Collet, J. I. van Hemert, and M. Tomassini, Eds., Lausanne, Switzerland, 2005, vol. 3447, Lecture Notes in Computer Science, pp. 38-49.
28. R. P. Sałustowicz and J. Schmidhuber, "Probabilistic incremental program evolution: Stochastic search through program space," in Machine Learning: ECML-97, M. van Someren and G. Widmer, Eds. Berlin, Germany: Springer-Verlag, 1997, vol. 1224, pp. 213-220.
29. R. Santana, "Estimation of distribution algorithms with Kikuchi approximations," Evol. Comput., vol. 13, no. 1, pp. 67-97, 2005.
30. K. Sastry and D. E. Goldberg, "Probabilistic model building and competent genetic programming," in Genetic Programming Theory and Practice, R. L. Riolo and B. Worzel, Eds. Norwell, MA: Kluwer, 2003, ch. 13, pp. 205-220.
31. G. Schwarz, "Estimating the dimension of a model," The Annals of Statistics, vol. 6, no. 2, pp. 461-464, 1978.
32. Y. Shan, R. I. McKay,H.A. Abbass, and D. Essam, "Program evolution with explicit learning: A new framework for program automatic synthesis," in Proc. 2003 Congr. Evol. Comput. CEC2003, R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam, and T. Gedeon, Eds., Canberra, 2003, pp. 1639-1646.
33. Y. Shan, R. I. McKay, R. Baxter, H. Abbass, D. Essam, and N. X. Hoai, "Grammar model-based program evolution," in Proc. 2004 IEEE Congr. Evol. Comput., G. Greenwood, Ed., Portland, OR, 2004, pp. 478-485.
34. M. Wineberg and F. Oppacher, "A representation scheme to perform program induction in a canonical genetic algorithm," in Lecture Notes in Computer Science, Y. Davidor, H.-P. Schwefel, and R. Männer, Eds. Berlin, Germany: Springer-Verlag, 1994, vol. 866, Parallel Problem Solving From Nature III, pp. 292-301.
35. K. Yanai and H. Iba, "Estimation of distribution programming based on Bayesian network," in Proc. 2003 Congr. Evol. Comput. CEC2003, R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam, and T. Gedeon, Eds., Canberra, pp. 1618-1625.
36. K. Yanai and H. Iba, "Program evolution by integrating EDP and GP," in GECCO 2004: Proc. 2004 Conf. Genetic Evol. Comput., K. Deb et al., Ed., Seattle, WA, 2004, pp. 774-785.