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GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
15 years 6 months ago
Why evolution is not a good paradigm for program induction: a critique of genetic programming
We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are ...
John R. Woodward, Ruibin Bai
BMCBI
2006
119views more  BMCBI 2006»
14 years 11 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 years 5 months ago
Constructive induction and genetic algorithms for learning concepts with complex interaction
Constructive Induction is the process of transforming the original representation of hard concepts with complex interaction into a representation that highlights regularities. Mos...
Leila Shila Shafti, Eduardo Pérez
ECCV
2002
Springer
16 years 1 months ago
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image...
Feng Han, Zhuowen Tu, Song Chun Zhu
BMCBI
2008
152views more  BMCBI 2008»
14 years 11 months ago
Predicting biological system objectives de novo from internal state measurements
Background: Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For examp...
Erwin P. Gianchandani, Matthew A. Oberhardt, Antho...