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GECCO
2010
Springer
129views Optimization» more  GECCO 2010»
15 years 2 months ago
A probabilistic functional crossover operator for genetic programming
The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated combinati...
Josh C. Bongard
GECCO
2010
Springer
152views Optimization» more  GECCO 2010»
15 years 2 months ago
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the “toolbox” used by ...
Jean-Baptiste Mouret, Stéphane Doncieux, Be...
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
15 years 2 months ago
Evolving stochastic processes using feature tests and genetic programming
The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest v...
Brian J. Ross, Janine H. Imada
GECCO
2009
Springer
15 years 2 months ago
Problem decomposition using indirect reciprocity in evolved populations
Evolutionary problem decomposition techniques divide a complex problem into simpler subproblems, evolve individuals to produce subcomponents that solve the subproblems, and then a...
Heather Goldsby, Sherri Goings, Jeff Clune, Charle...
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 2 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...