Efficient linkage discovery by limited probing

Evolutionary Computation

Volumn 12, Issue 4, 2004, Pages 517-545

  Heckendorn, Robert B ^a   Wright, Alden H ^b  

^a University of Idaho   (United States)

^b UNIVERSITY OF MONTANA   (United States)

Author keywords

Embedded landscapes; Epistasis; Linkage; MAXSAT; Probing; Walsh analysis

Indexed keywords

EMBEDDED LANDSCAPES; EPISTASIS; MAXSAT; WALSH ANALYSIS;

COMPUTATIONAL COMPLEXITY; ESTIMATION; GENETIC ALGORITHMS; PERTURBATION TECHNIQUES; PROBLEM SOLVING;

FUNCTION EVALUATION;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; GENETIC EPISTASIS; GENETIC LINKAGE; HUMAN; MOLECULAR COMPUTER;

ALGORITHMS; COMPUTERS, MOLECULAR; EPISTASIS, GENETIC; HUMANS; LINKAGE (GENETICS); MODELS, GENETIC;

EID: 10444289823     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/1063656043138914     Document Type: Article

References (29)

1. Braunstein, A., Mezard, M., Weigt, M., and Zecchina, R. (2003). Constraint satisfaction by survey propagation. arXiv.org:Condensed Matter Abstracts http://arxiv.org/abs/cond-mat/0212451.
2. Brodie, E. D. (2000). Why Evolutionary Genetics Doesn't Always Add Up, pages 3-19. Oxford University Press, Oxford, England.
3. Chen, Y. P. and Goldberg, D. E. (2003). Tightness time for the linkage learning genetic algorithm. In et. al., E. C.-P., editor, Genetic and Evolutionary Computation - GECCO 2003, pages 837-849. Springer.
4. de Bonet, J. S., Isbell, Jr., C. L., and Viola, P. (1997). MIMIC: Finding optima by estimating probability densities. In et. al., M. C. M., editor, Advances in Neural Information Processing Systems, volume 9, page 424. MIT Press.
5. Deb, K. and Goldberg, D. E. (1992). Analyzing deception in trap functions. In FOGA2, pages 93-108, Palo Alto, CA. Morgan Kaufmann Publishers, Inc.
6. Gao, Y. (2003). Space complexity of estimation of distribution algorithms. Technical report, University of Alberta.
7. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing, Co., Reading, MA.
8. Goldberg, D. E., Deb, K., Kargupta, H., and Harik, G. (1993). Rapid, accurate optimization of difficult optimization problems using fast messy genetic algorithms. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 56-64, San Mateo, California. Morgan Kaufman.
9. Harik, G. R. (1999). Linkage learning via probabilistic modeling in the ECGA. Technical report, University of Illinois. IlliGAL technical report 99010.
10. Harik, G. R., Cantú-Paz, E., Goldberg, D. E., and Miller, B. L. (1999). The gambler's ruin problem, generic algorithms, and the sizing of populations. Evolutionary Computation, 7(3):231-253.
11. Harik, G. R. and Goldberg, D. E. (2000). Learning linkage through probabilistic expression. Computer Methods in Applied Mechanics and Engineering, 186:295-310.
12. Heckendorn, R. B. (1999). Walsh Analysis, Epistasis, and Optimization Problem Difficulty for Evolutionary Algorithms. PhD thesis, Colorado State University, Department of Computer Science, Fort Collins, Colorado.
13. Heckendorn, R. B. (2002). Embedded landscapes. Evolutionary Computation, 10(4):345-376.
14. Heckendorn, R. B. and Whitley, D. (1999). Predicting epistasis from mathematical models. Evolutionary Computation, 7(1):69-101.
15. Hogg, T., Huberman, B. A., and Williams, C. P. (1996). Special issue SAT problems. Artificial Intelligence, 81(1-2).
16. Kargupta, H. (1996). The gene expression messy genetic algorithm. In International Conference on Evolutionary Computation, pages 814-819.
17. Kargupta, H. and Park, B. (2001). Gene expression and fast construction of distributed evolutionary representation. Evolutionary Computation, 9(l):43-69.
18. Kauffman, S. A. (1993). The Origins of Order. Oxford University Press, Oxford, England.
19. Mühlenbein, H. and Mahnig, T. (1999). Convergence theory and application of the factorized distribution algorithm. Journal of Computing and Information Technology, 7(1):19-32.
20. Mühlenbein, H., Mahnig, T., and Rodriguez, A. O. (1999). Schemata, distributions and graphical models in evolutionary optimization. J. of Heuristics, 5:215-247.
21. Munetomo, M. (2002a), Linkage identification based on epistasis measures to realize efficient genetic algorithms. In Proceedings of the Congress on Evolutionary Computation (CEC) 2002, pages 1332-1337. IEEE Press.
22. Munetomo, M. (2002b). Linkage identification with epistasis measures considering monotonicity conditions. In Proceedings of the 4th Asia-Pacific conference on simulated annealing and learning (SEAL-02), pages 550-554.
23. Munetomo, M. and Goldberg, D. E. (1999a). Identifying linkage groups by non-linearity / non-monotonicity detection. In et. al., W. B., editor, Proc. of the Genetic and Evolutionary Computation Conference, volume 1, pages 433-440, Palo Alto, CA. Morgan Kaufmann Publishers, Inc.
24. Munetomo, M. and Goldberg, D. E. (1999b). Linkage identification by nonmonotonicity detection for overlapping functions. Evolutionary Computation, 7(4):377-398.
25. Niven, I. (1965). Mathematics of Choice. Mathematical Association of America.
26. Papadimitriou, C. H. (1994). Computational Complexity. Addison-Wesley Publishing, Co.
27. Pelikan, M., Goldberg, D. E., and Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., iela, M. J., and Smith, R. E., editors, Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, volume I, pages 525-532, Orlando, FL. Morgan Kaufmann Publishers, San Francisco, CA.
28. Pelikan, M., Goldberg, D. E., and Cantú-Paz, E. (2000). Bayesian optimization algorithm, population sizing, and time to convergence. IlliGAL Report No. 2000001, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL.
29. Rana, S., Heckendorn, R. B., and Whitley, D. (1998). A tractable Walsh analysis of SAT and its implications for genetic algorithms. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 392-397, Menlo Park, CA. AAAI Press.