Convergence time for the linkage learning genetic algorithm

Evolutionary Computation

Volumn 13, Issue 3, 2005, Pages 279-302

  Chen, Ying Ping ^a   Goldberg, David E ^b  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Convergence time; Genetic algorithms; Genetic linkage; Linkage learning; Linkage learning genetic algorithm; Sequential behavior; Tightness time

Indexed keywords

ALGORITHM; ARTICLE; EVOLUTION; GENETICS; LEARNING;

ALGORITHMS; EVOLUTION; GENETICS; LEARNING;

EID: 25144449596     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/1063656054794806     Document Type: Article

References (49)

1. Ackley, D. H. (1987). A connectionist machine for genetic hill climbing. Boston: Kluwer Academic.
2. Andre, D., & Teller, A. (1996). A study in program response and the negative effects of introns in genetic programming. In Proceedings of the First Annual Conference on Genetic Programming (GP 96) (pp. 12-20). MIT Press.
3. Baluja, S. (1994). Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning (Tech. Rep. No. CMU-CS-94-163). Pittsburgh, PA: Carnegie Mellon University.
4. Baluja, S., & Davies, S. (1997). Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. In Proceedings of the Fourteenth International Conference on Machine Learning (pp. 30-38). Morgan Kaufmann Publishers, Inc.
5. Bonet, J. S. D., Isbell, C., & Viola, P. (1996). MIMIC: Finding optima by estimating probability densities. Advances in Neural Information Processing Systems, 9, 424-430.
6. Bosman, P. A., & Thierens, D. (2001). Advancing continuous IDEAs with mixture distributions and factorization selection metrics. In Proceedings of the Optimization by Building and Using Probabilistic Models Workshop at GECCO-2001 (pp. 208-202). GECCO.
7. Chen, Y.-p., & Goldberg, D. E. (2002). Introducing start expression genes to the linkage learning genetic algorithm. Lecture Notes in Computer Science (LNCS), 2439, 351-360. (Also IlliGAL Report No. 2002007).
8. Chen, Y.-p., & Goldberg, D. E. (2003). Tightness time for the linkage learning genetic algorithm. Lecture Notes in Computer Science, 2723, 837-849. (Also IlliGAL Report No. 2003002).
9. Deb, K., & Goldberg, D. E. (1993). Analyzing deception in trap functions. In Foundations of Genetic Algorithms 2 (pp. 93-108). Morgan Kaufmann Publishers, Inc.
10. Edmonds, J. (1967). Optimum branchings. Journal of Research of the National Bureau of Standards, 71B, 233-240.
11. Goldberg, D. E. (2002, June). The design of innovation: Lessons from and for competent genetic algorithms, Volume 7 of Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers. ISBN: 1-4020-7098-5.
12. Goldberg, D. E., Deb, K., Kargupta, H., & Harik, G. (1993). Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA-93) (pp. 56-64). Morgan Kaufmann Publishers, Inc. (Also IlliGAL Report No. 93004).
13. Goldberg, D. E., Deb, K., & Korb, B. (1990). Messy genetic algorithms revisited: Studies in mixed size and scale. Complex Systems, 4(4), 415-444.
14. Goldberg, D. E., Deb, K., & Thierens, D. (1993). Toward a better understanding of mixing in genetic algorithms. Journal of the Society of Instrument and Control Engineers, 32(1), 10-16. (Also IlliGAL Report No. 92009).
15. Goldberg, D. E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5), 493-530. (Also TCGA Report No. 89003).
16. Harik, G., Cantú-Paz, E., Goldberg, D. E., & Miller, B. L. (1997). The gambler's ruin problem, genetic algorithms, and the sizing of populations. In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (pp. 7-12). IEEE Publishers.
17. Harik, G., Cantuú-Paz, E., Goldberg, D. E., & Miller, B. L. (1999). The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3), 231-253.
18. Harik, G. R. (1997). Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. Doctoral dissertation, University of Michigan, Ann Arbor, MI. (Also IlliGAL Report No. 97005).
19. Harik, G. R. (1999). Linkage learning via probabilistic modeling in the ECGA (IlliGAL Report No. 99010). Urbana, IL: University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory.
20. Harik, G. R., & Goldberg, D. E. (1996). Learning linkage. In Foundations of Genetic Algorithms 4 (pp. 247-262). Morgan Kaufmann Publishers, Inc. (Also IlliGAL Report No. 96006).
21. Harik, G. R., & Goldberg, D. E. (2000, June). Learning linkage through probabilistic expression. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 295-310.
22. Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999). The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3(4), 287-297.
23. Heckerman, D., Geiger, D., & Chickering, M. (1994). Learning Bayesian networks (Tech. Rep. No. MSR-TR-94-09). Redmond, WA: Microsoft Research.
24. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press. ISBN: 0-262-58111-6.
25. Iba, H., & Terao, M. (2000). Controlling effective introns for multi-agent learning by genetic programming. In Proceedings of Genetic and Evolutionary Computation Conference 2000 (GECCO-2000) (pp. 419-426). Morgan Kaufmann Publishers, Inc.
26. Kargupta, H. (1996). The gene expression messy genetic algorithm. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (pp. 814-819). IEEE Publishers.
27. Larrañaga, P., & Lozano, J. A. (2001, October). Estimation of distribution algorithms: A new tool for evolutionary computation, Volume 2 of Genetic algorithms and evolutionary computation. Boston, MA: Kluwer Academic Publishers. ISBN: 0-7923-7466-5.
28. Levenick, J. R. (1991). Inserting introns improves genetic algorithm success rate: Taking a cue from biology. In Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA-91) (pp. 123-127). Morgan Kaufmann Publishers, Inc.
29. Levenick, J. R. (1995). Metabits: Generic endogenous crossover control. In Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA-95) (pp. 88-95). Morgan Kaufmann Publishers, Inc.
30. Marascuilo, L. A., & McSweeney, M. (1977). Nonparametric and distribution-free methods for the social sciences. CA: Brooks/Cole Publishing Company.
31. Mühlenbein, H., & Mahnig, T. (1999). FDA - a scalable evolutionary algorithm for the optimization for the optimization of additively decomposed functions. Evolutionary Computation, 7(4), 353-376.
32. Mühlenbein, H., & Paaß, G. (1996). From recombination of genes to the estimation of distributions I. Binary parameters. Lecture Notes in Computer Science, 1141, 178-187.
33. Munetomo, M. (2002a). Linkage identification based on epistasis measures to realize efficient genetic algorithms. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002) (pp. 1332-1337). IEEE Publishers.
34. Munetomo, M. (2002b). Linkage identification with epistasis measure considering monotonicity conditions. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL2002) (pp. 550-554). IEEE Publishers.
35. Munetomo, M., & Goldberg, D. E. (1998). Identifying linkage by nonlinearity check (IlliGAL Report No. 98012). Urbana, IL: University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory.
36. Munetomo, M., & Goldberg, D. E. (1999a). Identifying linkage groups by nonlinearity/nonmonotonicity detection. In Proceedings of Genetic and Evolutionary Computation Conference 1999 (GECCO-99) (pp. 433-440). Morgan Kaufmann Publishers, Inc.
37. Munetomo, M., & Goldberg, D. E. (1999b). Linkage identification by non-monotonicity detectio for overlapping functions. Evolutionary Computation, 7(4), 377-398. (Also IlliGAL Report No. 99005).
38. Nordin, P., Francone, F., & Banzhaf, W. (1996, October). Explicitly defined introns and destructive crossover in genetic programming. In Angeline, P. J., & Kinnear, Jr., K. E. (Eds.), Advances in Genetic Programming, Volume 2 (Chapter 6, pp. 111-134). MIT Press.
39. Pelikan, M., Goldberg, D. E., & Cantú-Paz, E. (2000). Linkage problem, distribution estimation, and bayesian networks. Evolutionary Computation, 8(3), 311-341. (Also IlliGAL Report No. 98013).
40. Pelikan, M., Goldberg, D. E., & Lobo, F. G. (2002). A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 22(1), 5-20. (Also IlliGAL Report No. 99018).
41. Pelikan, M., & Mühlenbein, H. (1999). The bivariate marginal distribution algorithm. In Advances in Soft Computing-Engineering Design and Manufacturing (pp. 521-535). Springer.
42. Schaffer, J. D., & Morishima, A. (1987). An adaptive crossover distribution mechanism for genetic algorithms. In Proceedings of the Second International Conference on Genetic Algorithms (ICGA-87) (pp. 36-40). Morgan Kaufmann Publishers, Inc.
43. Smith, J. (2002). On appropriate adaptation levels for the learning of gene linkage. Genetic Programming and Evolvable Machines, 3(2), 129-155.
44. Smith, J., & Fogarty, T. C. (1996). Recombination strategy adaptation via evolution of gene linkage. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (pp. 826-831). IEEE Publishers.
45. Thierens, D. (1995). Analysis and design of genetic algorithms. Doctoral dissertation, Katholieke Universiteit Leuven, Leuven, Belgium.
46. Thierens, D., & Goldberg, D. E. (1993). Mixing in genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA-93) (pp. 38-45). Morgan Kaufmann Publishers, Inc.
47. Thierens, D., Goldberg, D. E., & Pereira, Â. G. (1998). Domino convergence, drift, and the temporal-salience structure of problems. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation (pp. 535-540). IEEE Publishers.
48. Wineberg, M., & Oppacher, F. (1996). The benefits of computing with introns. In Proceedings of the First Annual Conference on Genetic Programming (GP 96) (pp. 410-415). MIT Press.
49. Wu, A. S., Lindsay, R. K., & Smith, M. D. (1994). Studies on the effect of non-coding segments on the genetic algorithm. In Proceedings of the Sixth IEEE Conference on Tools with Artificial Intelligence (pp. 744-747). IEEE Publishers.