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» Using Machine Learning to Focus Iterative Optimization
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GECCO
2004
Springer
147views Optimization» more  GECCO 2004»
15 years 8 months ago
A Demonstration of Neural Programming Applied to Non-Markovian Problems
Genetic programming may be seen as a recent incarnation of a long-held goal in evolutionary computation: to develop actual computational devices through evolutionary search. Geneti...
Gabriel Catalin Balan, Sean Luke
GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
15 years 9 months ago
Genetically programmed learning classifier system description and results
An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
Gregory Anthony Harrison, Eric W. Worden
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
15 years 8 months ago
A statistical learning theory approach of bloat
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of sy...
Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche, ...
ISDA
2009
IEEE
15 years 9 months ago
Improving Academic Performance Prediction by Dealing with Class Imbalance
Abstract—This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine lear...
Nguyen Thai-Nghe, Andre Busche, Lars Schmidt-Thiem...
COLT
2008
Springer
15 years 4 months ago
Model Selection and Stability in k-means Clustering
Clustering Stability methods are a family of widely used model selection techniques applied in data clustering. Their unifying theme is that an appropriate model should result in ...
Ohad Shamir, Naftali Tishby