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» Seeding Genetic Programming Populations
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SOCO
2010
Springer
13 years 2 months ago
Designing multilayer perceptrons using a Guided Saw-tooth Evolutionary Programming Algorithm
In this paper, a diversity generating mechanism is proposed for an Evolutionary Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers ...
Pedro Antonio Gutiérrez, César Herv&...
BMCBI
2008
135views more  BMCBI 2008»
13 years 7 months ago
Fregene: Simulation of realistic sequence-level data in populations and ascertained samples
Background: FREGENE simulates sequence-level data over large genomic regions in large populations. Because, unlike coalescent simulators, it works forwards through time, it allows...
Marc Chadeau-Hyam, Clive J. Hoggart, Paul F. O'Rei...
BMCBI
2007
163views more  BMCBI 2007»
13 years 7 months ago
Strainer: software for analysis of population variation in community genomic datasets
Background: Metagenomic analyses of microbial communities that are comprehensive enough to provide multiple samples of most loci in the genomes of the dominant organism types will...
John M. Eppley, Gene W. Tyson, Wayne M. Getz, Jill...
GECCO
2008
Springer
126views Optimization» more  GECCO 2008»
13 years 8 months ago
The impact of population size on code growth in GP: analysis and empirical validation
The crossover bias theory for bloat [18] is a recent result which predicts that bloat is caused by the sampling of short, unfit programs. This theory is clear and simple, but it ...
Riccardo Poli, Nicholas Freitag McPhee, Leonardo V...
GECCO
2005
Springer
151views Optimization» more  GECCO 2005»
14 years 1 months ago
Backward-chaining genetic programming
Tournament selection is the most frequently used form of selection in genetic programming (GP). Tournament selection chooses individuals uniformly at random from the population. A...
Riccardo Poli, William B. Langdon