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AAAI
1998
13 years 11 months ago
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems.STAGElearns an evaluation function which predicts the outcom...
Justin A. Boyan, Andrew W. Moore
FSTTCS
2004
Springer
14 years 3 months ago
Join Algorithms for the Theory of Uninterpreted Functions
The join of two sets of facts, E1 and E2, is defined as the set of all facts that are implied independently by both E1 and E2. Congruence closure is a widely used representation f...
Sumit Gulwani, Ashish Tiwari, George C. Necula
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
14 years 3 months ago
A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...
Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pah...
CGI
1999
IEEE
14 years 2 months ago
Evolutionary Optimization of Functionally Defined Shapes: Case Study of Natural Optical Objects
This paper focuses on an approach to modeling shapes through the use of evolutionary optimization or genetic algorithms for functionally represented geometric objects. This repres...
Vladimir V. Savchenko, Alexander A. Pasko
CORR
2008
Springer
77views Education» more  CORR 2008»
13 years 10 months ago
Optimal hash functions for approximate closest pairs on the n-cube
One way to find closest pairs in large datasets is to use hash functions [6], [12]. In recent years locality-sensitive hash functions for various metrics have been given: projecti...
Daniel M. Gordon, Victor Miller, Peter Ostapenko