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AIPS
2008
15 years 1 days ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein
JCC
2002
73views more  JCC 2002»
14 years 9 months ago
Linear scaling approaches to quantum macromolecular similarity: Evaluating the similarity function
: The evaluation of the electron density based similarity function scales quadratically with respect to the size of the molecules for simplified, atomic shell densities. Due to the...
Pere Constans
CPAIOR
2007
Springer
15 years 3 months ago
On Boolean Functions Encodable as a Single Linear Pseudo-Boolean Constraint
A linear pseudo-Boolean constraint (LPB) is an expression of the form a1 · 1 + . . . + am · m ≥ d, where each i is a literal (it assumes the value 1 or 0 depending on whether a...
Jan-Georg Smaus
GECCO
2008
Springer
131views Optimization» more  GECCO 2008»
14 years 10 months ago
Rigorous analyses of fitness-proportional selection for optimizing linear functions
Rigorous runtime analyses of evolutionary algorithms (EAs) mainly investigate algorithms that use elitist selection methods. Two algorithms commonly studied are Randomized Local S...
Edda Happ, Daniel Johannsen, Christian Klein, Fran...
ADCM
2006
74views more  ADCM 2006»
14 years 9 months ago
Linearly constrained reconstruction of functions by kernels with applications to machine learning
This paper investigates the approximation of multivariate functions from data via linear combinations of translates of a positive definite kernel from a reproducing kernel Hilbert...
Robert Schaback, J. Werner