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GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 1 months ago
Preventing overfitting in GP with canary functions
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Nate Foreman, Matthew P. Evett
GECCO
2008
Springer
126views Optimization» more  GECCO 2008»
13 years 8 months ago
The impact of population size on code growth in GP: analysis and empirical validation
The crossover bias theory for bloat [18] is a recent result which predicts that bloat is caused by the sampling of short, unfit programs. This theory is clear and simple, but it ...
Riccardo Poli, Nicholas Freitag McPhee, Leonardo V...
GECCO
2007
Springer
158views Optimization» more  GECCO 2007»
13 years 11 months ago
A GP neutral function for the artificial ANT problem
This paper introduces a function that increases the amount of neutrality (inactive code in Genetic Programming) for the Artificial Ant Problem. The objective of this approach is t...
Esteban Ricalde, Katya Rodríguez-Váz...
ENTCS
2008
129views more  ENTCS 2008»
13 years 7 months ago
The York Abstract Machine
Abstract Machine Greg Manning1 Detlef Plump2 Department of Computer Science The University of York, UK duce the York Abstract Machine (YAM) for implementing the graph programming ...
Greg Manning, Detlef Plump
VIP
2003
13 years 9 months ago
Web-based Multimedia GP Medical System
This paper introduced GP-Soft, a powerful new patient management system, which is designed to improve the records management for clinic, nursing home and hospital. GP-Soft is a co...
Chee Chern Lim, Man Hing Yu, Jesse J. Jin