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KI
2007
Springer
13 years 9 months ago
A General Framework for Encoding and Evolving Neural Networks
Abstract. In this paper we present a novel general framework for encoding and evolving networks called Common Genetic Encoding (CGE) that can be applied to both direct and indirect...
Yohannes Kassahun, Jan Hendrik Metzen, Jose de Gea...
GECCO
2010
Springer
152views Optimization» more  GECCO 2010»
13 years 8 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
2010
Springer
181views Optimization» more  GECCO 2010»
13 years 8 months ago
Evolving neural networks in compressed weight space
We propose a new indirect encoding scheme for neural networks in which the weight matrices are represented in the frequency domain by sets of Fourier coefficients. This scheme exp...
Jan Koutnik, Faustino J. Gomez, Jürgen Schmid...
GECCO
2004
Springer
134views Optimization» more  GECCO 2004»
13 years 9 months ago
A Descriptive Encoding Language for Evolving Modular Neural Networks
Evolutionary algorithms are a promising approach for the automated design of artificial neural networks, but they require a compact and efficient genetic encoding scheme to repres...
Jae-Yoon Jung, James A. Reggia
GECCO
2007
Springer
158views Optimization» more  GECCO 2007»
13 years 9 months ago
A novel generative encoding for exploiting neural network sensor and output geometry
A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example,...
David B. D'Ambrosio, Kenneth O. Stanley