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CORR
2006
Springer
153views Education» more  CORR 2006»
13 years 8 months ago
Genetic Programming, Validation Sets, and Parsimony Pressure
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
Christian Gagné, Marc Schoenauer, Marc Pari...
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
14 years 1 months ago
Genetic programming: parametric analysis of structure altering mutation techniques
We hypothesize that the relationship between parameter settings, speci cally parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programmin...
Alan Piszcz, Terence Soule
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolutionary computation and the c-value paradox
The C-value Paradox is the name given in biology to the wide variance in and often very large amount of DNA in eukaryotic genomes and the poor correlation between DNA length and p...
Sean Luke
EUROGP
2009
Springer
132views Optimization» more  EUROGP 2009»
14 years 3 months ago
A Statistical Learning Perspective of Genetic Programming
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 GP from the perspec...
Nur Merve Amil, Nicolas Bredeche, Christian Gagn&e...
NC
1998
138views Neural Networks» more  NC 1998»
13 years 9 months ago
Parallel Adaptive Genetic Algorithm
In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is exte...
Leo Budin, Marin Golub, Domagoj Jakobovic