Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 851-858

  Pelikan, Martin ^a   Sastry, Kumara ^b   Goldberg, David E ^c   Butz, Martin V ^d   Hauschild, Mark ^a  

^a UNIVERSITY OF MISSOURI ST LOUIS   (United States)

^b INTEL CORPORATION   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Crossover; Fitness landscape; Genetic algorithm; hBOA; Hierarchical boa; Hybridization; NK fitness landscape; Performance analysis; Problem difficulty; Scalability

Indexed keywords

ESTIMATION OF DISTRIBUTION ALGORITHMS; FITNESS LANDSCAPE; HBOA; HIERARCHICAL BOA; NEAREST-NEIGHBOR INTERACTIONS; PERFORMANCE ANALYSIS; POLYNOMIAL-TIME; PROBLEM INSTANCES; RANDOM PROBLEM;

GENETIC ALGORITHMS; POLYNOMIAL APPROXIMATION; SCALABILITY; SYSTEMS ENGINEERING;

HEALTH;

EID: 72749108865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1570018     Document Type: Conference Paper

References (36)

1. H. E. Aguirre and K. Tanaka. Genetic algorithms on nk-landscapes: Effects of selection, drift, mutation, and recombination. In G. R. Raidl et al., editors, Applications of Evolutionary Computing: EvoWorkshops 2003, pages 131-142, 2003.
2. L. Altenberg. NK landscapes. In T. Bäck, D. B. Fogel, and Z. Michalewicz, editors, Handbook of Evolutionary Computation, pages B2.7:5-10. Institute of Physics Publishing and Oxford University Press, 1997.
3. J. W. Barnes, B. Dimova, S. P. Dokov, and A. Solomon. The theory of elementary landscapes. Appl. Math. Lett., 16(3):337-343, 2003.
4. D. M. Chickering, D. Heckerman, and C. Meek. A Bayesian approach to learning Bayesian networks with local structure. Technical Report MSR-TR-97-07, Microsoft Research, Redmond, WA, 1997.
5. S.-S. Choi, K. Jung, and J. H. Kim. Phase transition in a random NK landscape model. Genetic and Evolutionary Computation Conf. (GECCO-2005), pages 1241-1248, 2005.
6. D. J. Coffin and R. E. Smith. Why is parity hard for estimation of distribution algorithms? Genetic and Evolutionary Computation Conf. (GECCO-2007), pages 624-624, 2007.
7. N. Friedman and M. Goldszmidt. Learning Bayesian networks with local structure. In M. I. Jordan, editor, Graphical models, pages 421-459. MIT Press, 1999.
8. Y. Gao and J. C. Culberson. An analysis of phase transition in NK landscapes. Journal of Artificial Intelligence Research (JAIR), 17:309-332, 2002.
9. D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA, 1989.
10. D. E. Goldberg. The design of innovation: Lessons from and for competent genetic algorithms. Kluwer, 2002.
11. D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6:333-362, 1992.
12. D. E. Goldberg and M. Rudnick. Genetic algorithms and the variance of fitness. Complex Systems, 5(3):265-278, 1991.
13. G. R. Harik. Finding multimodal solutions using restricted tournament selection. Int. Conf. on Genetic Algorithms (ICGA-95), pages 24-31, 1995.
14. G. R. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Int. Conf. on Evolutionary Computation (ICEC-97), pages 7-12, 1997.
15. G. R. Harik and D. E. Goldberg. Learning linkage. Foundations of Genetic Algorithms, 4:247-262, 1996.
16. D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Technical Report MSR-TR-94-09, Microsoft Research, Redmond, WA, 1994.
17. J. H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI, 1975.
18. S. Kauffman. Adaptation on rugged fitness landscapes. In D. L. Stein, editor, Lecture Notes in the Sciences of Complexity, pages 527-618. Addison Wesley, 1989.
19. S. Kauffman. The origins of Order: Self-organization and selection in evolution. Oxford University Press, 1993.
20. H. Mühlenbein and T. Mahnig. Convergence theory and applications of the factorized distribution algorithm. Journal of Computing and Information Technology, 7(1):19-32, 1998.
21. M. Pelikan. Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer, 2005.
22. M. Pelikan and D. E. Goldberg. Escaping hierarchical traps with competent genetic algorithms. Genetic and Evolutionary Computation Conf. (GECCO-2001), pages 511-518, 2001.
23. M. Pelikan, K. Sastry, M. V. Butz, and D. E. Goldberg. Performance of evolutionary algorithms on random decomposable problems. Parallel Problem Solving from Nature, pages 788-797, 2006.
24. M. Pelikan, K. Sastry, M. V. Butz, and D. E. Goldberg. Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes. Genetic and Evolutionary Computation Conf. (GECCO-2008), pages 1033-1040, 2008.
25. M. Pelikan, K. Sastry, and D. E. Goldberg. Scalability of the Bayesian optimization algorithm. International Journal of Approximate Reasoning, 31(3):221-258, 2002.
26. M. Pelikan, K. Sastry, D. E. Goldberg, M. V. Butz, and M. Hauschild. Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap. MEDAL Report No. 2009002, Missouri Estimation of Distribution Algorithms Laboratory, 2009.
27. F. Rothlauf. Representations for Genetic and Evolutionary Algorithms. Springer-Verlag, 2nd edition, 2006.
28. K. Sastry. Evaluation-relaxation schemes for genetic and evolutionary algorithms. Master's thesis, Univ. of Illinois at Urbana-Champaign, Urbana, IL, 2001.
29. K. Sastry, M. Pelikan, and D. E. Goldberg. Empirical analysis of ideal recombination on random decomposable problems. Genetic and Evolutionary Computation Conf. (GECCO-2007), pages 1388-1395, 2007.
30. D. Thierens. Analysis and design of genetic algorithms. PhD thesis, Katholieke Universiteit Leuven, Leuven, Belgium, 1995.
31. D. Thierens. Scalability problems of simple genetic algorithms. Evolutionary Computation, 7(4):331-352, 1999.
32. D. Thierens and D. Goldberg. Convergence models of genetic algorithm selection schemes. Parallel Problem Solving from Nature, pages 116-121, 1994.
33. D. Thierens and D. E. Goldberg. Mixing in genetic algorithms. Int. Conf. on Genetic Algorithms (ICGA-93), pages 38-45, 1993.
34. D. Thierens, D. E. Goldberg, and A. G. Pereira. Domino convergence, drift, and the temporal-salience structure of problems. Int. Conf. on Evolutionary Computation (ICEC-98), pages 535-540, 1998.
35. A. H. Wright, R. K. Thompson, and J. Zhang. The computational complexity of N-K fitness functions. IEEE Trans. on Evolutionary Computation, 4(4):373-379, 2000.
36. T.-L. Yu, K. Sastry, D. E. Goldberg, and M. Pelikan. Population sizing for entropy-based model building in estimation of distribution algorithms. Genetic and Evolutionary Computation Conf. (GECCO-2007), pages 601-608, 2007.