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» Evolving recurrent models using linear GP
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EUROGP
2007
Springer
126views Optimization» more  EUROGP 2007»
13 years 11 months ago
Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach
The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the ...
Michal Lemczyk, Malcolm I. Heywood
EVOW
2004
Springer
13 years 10 months ago
Evolving Regular Expression-Based Sequence Classifiers for Protein Nuclear Localisation
A number of bioinformatics tools use regular expression (RE) matching to locate protein or DNA sequence motifs that have been discovered by researchers in the laboratory. For exam...
Amine Heddad, Markus Brameier, Robert M. MacCallum
TEC
2008
139views more  TEC 2008»
13 years 6 months ago
Genetic Programming Approaches for Solving Elliptic Partial Differential Equations
In this paper, we propose a technique based on genetic programming (GP) for meshfree solution of elliptic partial differential equations. We employ the least-squares collocation pr...
Andras Sobester, Prasanth B. Nair, Andy J. Keane
GECCO
1999
Springer
142views Optimization» more  GECCO 1999»
13 years 11 months ago
Towards Byte Code Genetic Programming
This paper uses the GP paradigm to evolve linear genotypes (individuals) that consist of Java byte code. Our prototype GP system is implemented in Java using a standard Java devel...
Brad Harvey, James A. Foster, Deborah A. Frincke
GECCO
2005
Springer
204views Optimization» more  GECCO 2005»
14 years 15 days ago
Modeling systems with internal state using evolino
Existing Recurrent Neural Networks (RNNs) are limited in their ability to model dynamical systems with nonlinearities and hidden internal states. Here we use our general framework...
Daan Wierstra, Faustino J. Gomez, Jürgen Schm...