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GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
13 years 8 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Ashley George, Malcolm I. Heywood
GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
13 years 11 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
EVOW
1994
Springer
13 years 9 months ago
Genetic Approaches to Learning Recursive Relations
The genetic programming (GP) paradigm is a new approach to inductively forming programs that describe a particular problem. The use of natural selection based on a fitness ]unction...
Peter A. Whigham, Robert I. McKay
EUROGP
2004
Springer
145views Optimization» more  EUROGP 2004»
13 years 10 months ago
Toward an Alternative Comparison between Different Genetic Programming Systems
In this paper, we use multi-objective techniques to compare different genetic programming systems, permitting our comparison to concentrate on the effect of representation and sepa...
Nguyen Xuan Hoai, Robert I. McKay, Daryl Essam, Hu...
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
13 years 9 months ago
Evolution, development and learning using self-modifying cartesian genetic programming
Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This...
Simon Harding, Julian Francis Miller, Wolfgang Ban...