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» Iterative Filter Generation Using Genetic Programming
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GPEM
2000
121views more  GPEM 2000»
13 years 5 months ago
Bayesian Methods for Efficient Genetic Programming
ct. A Bayesian framework for genetic programming GP is presented. This is motivated by the observation that genetic programming iteratively searches populations of fitter programs ...
Byoung-Tak Zhang
GECCO
2007
Springer
293views Optimization» more  GECCO 2007»
13 years 11 months ago
Solving the artificial ant on the Santa Fe trail problem in 20, 696 fitness evaluations
In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate useful subr...
Steffen Christensen, Franz Oppacher
EUROGP
2007
Springer
144views Optimization» more  EUROGP 2007»
13 years 11 months ago
Fitness Landscape Analysis and Image Filter Evolution Using Functional-Level CGP
This work analyzes fitness landscapes for the image filter design problem approached using functional-level Cartesian Genetic Programming. Smoothness and ruggedness of fitness l...
Karel Slaný, Lukás Sekanina
EUROGP
1999
Springer
13 years 9 months ago
Genetic Programming as a Darwinian Invention Machine
Genetic programming is known to be capable of creating designs that satisfy prespecified high-level design requirements for analog electrical circuits and other complex structures...
John R. Koza, Forrest H. Bennett III, Oscar Stiffe...
GPEM
2010
180views more  GPEM 2010»
13 years 3 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