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CEC
2007
IEEE
14 years 1 months ago
Combine and compare evolutionary robotics and reinforcement Learning as methods of designing autonomous robots
—The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for...
Sergiu Goschin, Eduard Franti, Monica Dascalu, San...
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 2 months ago
Neural network ensembles for time series forecasting
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Victor M. Landassuri-Moreno, John A. Bullinaria
AROBOTS
2010
180views more  AROBOTS 2010»
13 years 7 months ago
Reactive direction control for a mobile robot: a locust-like control of escape direction emerges when a bilateral pair of model
Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant m...
Shigang Yue, Roger D. Santer, Yoshifumi Yamawaki, ...
IJCNN
2008
IEEE
14 years 1 months ago
Building meta-learning algorithms basing on search controlled by machine complexity
Abstract— Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Metalearning algorithm presented in this paper is universal and m...
Norbert Jankowski, Krzysztof Grabczewski
ICTAI
2008
IEEE
14 years 1 months ago
The Performance of Approximating Ordinary Differential Equations by Neural Nets
—The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of compu...
Josef Fojdl, Rüdiger W. Brause