Compact representations as a search strategy: Compression EDAs

Theoretical Computer Science

Volumn 361, Issue 1, 2006, Pages 57-71

  Toussaint, Marc ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Compression; Estimation of Distribution Algorithms; Evolutionary Algorithms; Factorial representations; Genotype phenotype mapping; Minimal description length

Indexed keywords

ADAPTIVE SYSTEMS; CONFORMAL MAPPING; ESTIMATION; EVOLUTIONARY ALGORITHMS; INTERACTIVE COMPUTER SYSTEMS; PROBLEM SOLVING; SEARCH ENGINES;

ESTIMATION-OF-DISTRIBUTION ALGORITHMS; FACTORIAL REPRESENTATIONS; GENOTYPE-PHENOTYPE MAPPING; MINIMAL DESCRIPTION LENGTH;

KNOWLEDGE REPRESENTATION;

EID: 33746837771     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tcs.2006.04.005     Document Type: Article

References (31)

1. Amari S. Information geometry on hierarchy of probability distributions. IEEE Trans. Inform. Theory 47 (2001) 1701-1711
2. S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical Report CMU-CS-94-163, Computer Science Department, Carnegie Mellon University, 1994.
3. S. Baluja, S. Davies, Using optimal dependency-trees for combinatorial optimization: learning the structure of the search space, in: Proc. 14th Int. Conf. on Machine Learning, ICML 1997, 1997, pp. 30-38.
4. L. Barbulescu, J.-P. Watson, D. Whitley, Dynamic representations and escaping local optima: improving genetic algorithms and local search, in: 17th National Conf. on Artificial Intelligence (AAAI), 2000, pp. 879-884.
5. Barron A., Rissanen J., and Yu B. The minimum description length principle in coding and modeling. IEEE Trans. Inform. Theory 44 (1998) 2743-2760
6. K. Burjorjee, J. Pollack, Theme preservation and the evolution of representation, in: Genetic and Evolutionary Computation Conference (GECCO 2005); Theory of Representations Workshop, 2005.
7. de Bonet J.S., Isbell Jr. C.L., and Viola P. MIMIC: finding optima by estimating probability densities. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems Vol. 9 (1997), The MIT Press, Cambridge, MA 424
8. E.D. de Jong, Representation development from Pareto-Coevolution, in: Genetic and Evolutionary Computation Conference, GECCO 2003, 2003, pp. 265-276.
9. G. Harik, Linkage learning via probabilistic modeling in the ECGA, Technical Report IlliGAL-97006, Illinois Genetic Algorithms Laborator, 1999.
10. G. Harik, F. Lobo, D. Goldberg, The compact genetic algorithm, Technical Report IlliGAL-97006, Illinois Genetic Algorithms Laborator, 1997.
11. Heckendorn R.B., and Wright A.H. Efficient linkage discovery by limited probing. Evolutionary Comput. 12 (2004) 517-545
12. G.S. Hornby, J.B. Pollack, The advantages of generative grammatical encodings for physical design, in: Proc. 2001 Cong. on Evolutionary Computation, CEC 2001, IEEE Press, New York, 2001, pp. 600-607.
13. Huynen M.A. Exploring phenotype space through neutral evolution. J. Molecular Evol. 43 (1996) 165-169
14. Hyvärinen A., and Oja E. Independent component analysis: algorithms and applications. Neural Networks 13 (2000) 411-430
15. E. Lehman, A. Shelat, Approximation algorithms for grammar-based compression, in: Proc. 13th Annu. ACM-SIAM Symposium on Discrete Algorithms, SODA 2002, ACM/SIAM, 2002, pp. 205-212.
16. Liepins G.E., and Vose M.D. Representation issues in genetic algorithms. J. Experimental Theoret. Artif. Intell. 2 (1990)
17. Nevill-Manning C.G., and Witten I.H. Identifying hierarchical structure in sequences: a linear-time algorithm. J. Artif. Intell. Res. 7 (1997) 67-82
18. Nordin P., and Banzhaf W. Complexity compression and evolution. In: Eshelman L. (Ed). Genetic Algorithms: Proc. Sixth Internat. Conf., ICGA 1995 (1995), Morgan Kaufmann, Pittsburgh 310-317
19. M. Pelikan, D.E. Goldberg, Hierarchical BOA solves Ising spin glasses and MAXSAT, in: Genetic and Evolutionary Computation Conf. (GECCO 2003), Springer, Berlin, 2003, pp. 1271-1282.
20. Pelikan M., Goldberg D.E., and Cantú-Paz E. Linkage problem, distribution estimation, and Bayesian networks. Evolution. Comput. 9 (2000) 311-340
21. M. Pelikan, D.E. Goldberg, F. Lobo, A survey of optimization by building and using probabilistic models, Technical Report IlliGAL-99018, Illinois Genetic Algorithms Laboratory, 1999.
22. Rothlauf F., and Goldberg D.E. Redundant representations in evolutionary computation. Evolution. Comput. 11 (2003) 381-415
23. Stephens C.R., and Vargas J.M. Effective fitness as an alternative paradigm for evolutionary computation I: general formalism. Genetic Programm. Evolvable Mach. 1 (2000) 363-378
24. M. Toussaint, Demonstrating the evolution of complex genetic representations: an evolution of artificial plants, in: Genetic and Evolutionary Comput. Conf., GECCO 2003, 2003, pp. 86-97.
25. Toussaint M. On the evolution of phenotypic exploration distributions. In: Cotta C., De Jong K., Poli R., and Rowe J. (Eds). Foundations of Genetic Algorithms 7 (FOGA VII) (2003), Morgan Kaufmann 169-182
26. M. Toussaint, The structure of evolutionary exploration: on crossover, buildings blocks, and Estimation-of-Distribution algorithms, in: Genetic and Evolutionary Comput. Conf., GECCO 2003, 2003, pp. 1444-1456.
27. M. Toussaint, Factorial representations to generate arbitrary search distributions, in: Genetic and Evolutionary Computation Conference, GECCO 2005; Theory of Representations workshop, ACM Press, Washington, DC, USA, 2005, pp. 339-345.
28. Vitányi P.M.B., and Li M. Minimum description length induction, Bayesianism, and Kolmogorov complexity. IEEE Trans. Inform. Theory IT-46 (2000) 446-464
29. R.A. Watson, J.B. Pollack, Hierarchically consistent test problems for genetic algorithms: summary and additional results, in: Late breaking Papers at the Genetic and Evolutionary Comput. Conf., 1999, pp. 292-297.
30. D. Whitley, S. Rana, R. Heckendorn, Representation issues in neighborhood search and evolutionary algorithms, in: Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, Wiley, New York, 1997, pp. 39-58.
31. A.H. Wright, R. Poli, C.R. Stephens, W.B. Langdon, S. Pulavarty, An estimation of distribution algorithm based on maximum entropy, in: Genetic and Evolutionary Computation Conference, GECCO 2004, Springer, Berlin, 2004, pp. 343-354.