MIMIC: Finding optima by estimating probability densities

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 424-430

  De Bonet, Jeremy S ^a   Isbell Jr , Charles L ^a   Viola, Paul ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ESTIMATION;

COMPLEX RELATIONSHIPS; COST LANDSCAPE; GLOBAL STRUCTURE; INPUT PARAMETER; OPTIMIZATION PROBLEMS; PROBABILITY DENSITIES; RANDOMIZED OPTIMIZATIONS; RANDOMIZED SEARCH;

STRUCTURAL OPTIMIZATION;

EID: 78049265488     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Baluja, S. and Caruana, R. (1995). Removing the genetics from the standard genetic algorithm. Technical report, Carnegie Mellon Univerisity.
2. Baum, E. B., Boneh, D., and Garrett, C. (1995). Where genetic algorithms excel. In Proceedings of the Conference on Computational Learning Theory, New York. Association for Computing Machinery.
3. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. The Michigan University Press.
4. Kirkpatrick, S., Gelatt, C, and Vecchi, M. (1983). Optimization by Simulated Annealing. Science, 220(4598):671-680.
5. Lang, K. (1995). Hill climbing beats genetic search on a boolean circuit synthesis problem of koza's. In Twelfth International Conference on Machine Learning.
6. Sabes, P. N. and Jordan, M. I. (1995). Reinforcement learning by probability matching. In David S. Touretzky, M. M. and Perrone, M., editors, Advances in Neural Information Processing, volume 8, Denver 1995. MIT Press, Cambridge.