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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...
GPEM
2010
180views more  GPEM 2010»
13 years 2 months ago
Developments in Cartesian Genetic Programming: self-modifying CGP
Abstract Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming....
Simon Harding, Julian F. Miller, Wolfgang Banzhaf
GECCO
2008
Springer
179views Optimization» more  GECCO 2008»
13 years 5 months ago
Developing neural structure of two agents that play checkers using cartesian genetic programming
A developmental model of neural network is presented and evaluated in the game of Checkers. The network is developed using cartesian genetic programs (CGP) as genotypes. Two agent...
Gul Muhammad Khan, Julian Francis Miller, David M....
GECCO
2007
Springer
160views Optimization» more  GECCO 2007»
13 years 10 months ago
Self-modifying cartesian genetic programming
In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is su...
Simon Harding, Julian Francis Miller, Wolfgang Ban...
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 7 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto