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» Unsupervised Problem Decomposition Using Genetic Programming
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ICML
2005
IEEE
15 years 10 months ago
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
Fei Sha, Lawrence K. Saul
107
Voted
FGCS
2010
119views more  FGCS 2010»
14 years 8 months ago
Characterizing fault tolerance in genetic programming
Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of computation. ...
Daniel Lombraña Gonzalez, Francisco Fern&aa...
GECCO
2006
Springer
218views Optimization» more  GECCO 2006»
15 years 1 months ago
Genetic programming with primitive recursion
When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters...
Stefan Kahrs
GECCO
2000
Springer
178views Optimization» more  GECCO 2000»
15 years 1 months ago
Fitness Sharing in Genetic Programming
This paper investigates fitness sharing in genetic programming. Implicit fitness sharing is applied to populations of programs. Three treatments are compared: raw fitness, pure fi...
Robert I. McKay
97
Voted
TCBB
2011
14 years 4 months ago
Accurate Construction of Consensus Genetic Maps via Integer Linear Programming
—We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which...
Yonghui Wu, Timothy J. Close, Stefano Lonardi