Using a priori knowledge to create probabilistic models for optimization-Reference-Cited by-同舟云学术

Using a priori knowledge to create probabilistic models for optimization

Published:2002-11 Issue:3 Volume:31 Page:193-220
ISSN:0888-613X
Container-title:International Journal of Approximate Reasoning
language:en
Short-container-title:International Journal of Approximate Reasoning

Author:

Baluja Shumeet

Publisher

Elsevier BV

Subject

Applied Mathematics,Artificial Intelligence,Theoretical Computer Science,Software

Reference46 articles.

1. S. Baluja, Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Technical Report CMUCS- 94-163, Carnegie Mellon University, Pittsburgh, PA, 1994

2. S. Baluja, An Empirical Comparison of Seven Iterative and Evolutionary Heuristics for Static Function Optimization Technical Report CMU-CS-95-193, Carnegie Mellon University, Pittsburgh, PA, 1995

3. Removing the genetics from the standard genetic algorithm;Baluja,1995

4. Using optimal dependency trees for combinatorial optimization: learning the structure of the search space;Baluja,1997

5. Fast probabilistic modeling for combinatorial optimization;Baluja,1998

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