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» Evolutionary Optimization of RBF Networks
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CCR
2010
156views more  CCR 2010»
13 years 8 months ago
Evolvable network architectures: what can we learn from biology?
There is significant research interest recently to understand the evolution of the current Internet, as well as to design clean-slate Future Internet architectures. Clearly, even ...
Constantine Dovrolis, J. Todd Streelman
GECCO
2007
Springer
158views Optimization» more  GECCO 2007»
14 years 2 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
GECCO
2007
Springer
155views Optimization» more  GECCO 2007»
14 years 2 months ago
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Markov Networks (also known as Markov Random Fields) have been proposed as a new approach to probabilistic modelling in Estimation of Distribution Algorithms (EDAs). An EDA employ...
Alexander E. I. Brownlee, John A. W. McCall, Deryc...
NOLISP
2007
Springer
14 years 2 months ago
Word Recognition with a Hierarchical Neural Network
In this paper we propose a feedforward neural network for syllable recognition. The core of the recognition system is based on a hierarchical architecture initially developed for ...
Xavier Domont, Martin Heckmann, Heiko Wersing, Fra...
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen