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» Evolving Artificial Neural Networks that Develop in Time
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CONNECTION
2004
117views more  CONNECTION 2004»
14 years 9 months ago
Structure and function of evolved neuro-controllers for autonomous robots
The Artificial Life approach to Evolutionary Robotics is used as a fundamental framework for the development of a modular neural control of autonomous mobile robots. The applied e...
Martin Hülse, Steffen Wischmann, Frank Pasema...
AI
2002
Springer
14 years 9 months ago
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...
Zhi-Hua Zhou, Jianxin Wu, Wei Tang
CEC
2008
IEEE
15 years 4 months ago
Creating edge detectors by evolutionary reinforcement learning
— In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is c...
Nils T. Siebel, Sven Grünewald, Gerald Sommer
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
15 years 1 months ago
The baldwin effect in developing neural networks
The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave...
Keith L. Downing
CIG
2006
IEEE
15 years 3 months ago
A Coevolutionary Model for The Virus Game
— In this paper, coevolution is used to evolve Artificial Neural Networks (ANN) which evaluate board positions of a two player zero-sum game (The Virus Game). The coevolved neura...
Peter I. Cowling, M. H. Naveed, M. A. Hossain