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GECCO
2009
Springer
164views Optimization» more  GECCO 2009»
8 years 2 months ago
Solving iterated functions using genetic programming
An iterated function f(x) is a function that when composed with itself, produces a given expression f(f(x))=g(x). Iterated functions are essential constructs in fractal theory and...
Michael D. Schmidt, Hod Lipson
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
8 years 2 months ago
Genetic programming for quantitative stock selection
We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Ying L. Becker, Una-May O'Reilly
GPEM
2010
134views more  GPEM 2010»
8 years 2 months ago
An ensemble-based evolutionary framework for coping with distributed intrusion detection
A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic p...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
GPEM
2010
180views more  GPEM 2010»
8 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
FGCS
2010
119views more  FGCS 2010»
8 years 2 months ago
Characterizing fault tolerance in genetic programming
Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of computation. ...
Daniel Lombraña Gonzalez, Francisco Fern&aa...
PRL
2007
200views more  PRL 2007»
8 years 3 months ago
Generative learning of visual concepts using multiobjective genetic programming
This paper introduces a novel method of visual learning based on Genetic Programming, which evolves a population of individuals (image analysis programs) that process attributed v...
Krzysztof Krawiec
ESWA
2008
141views more  ESWA 2008»
8 years 3 months ago
Classifier design with feature selection and feature extraction using layered genetic programming
This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic p...
Jung-Yi Lin, Hao-Ren Ke, Been-Chian Chien, Wei-Pan...
PRL
2002
213views more  PRL 2002»
8 years 3 months ago
Character preclassification based on genetic programming
This paper presents a learning system that uses genetic programming as a tool for automatically inferring the set of classification rules to be used during a preclassification sta...
Claudio De Stefano, Antonio Della Cioppa, Angelo M...
ISCI
1998
193views more  ISCI 1998»
8 years 3 months ago
A Parallel Implementation of Genetic Programming that Achieves Super-Linear Performance
: This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researc...
David Andre, John R. Koza
FUIN
1998
112views more  FUIN 1998»
8 years 3 months ago
A Historical Perspective on the Evolution of Executable Structures
Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in the evo...
Peter J. Angeline
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