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Neuroevolution

Neuroevolution, or neuro-evolution, is a form of machine learning that uses evolutionary algorithms to train artificial neural networks. It is most… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this… 
2017
2017
Among the issues discussed in this article the main problem concerns a neuro-fuzzy system (ANFIS) for controlling a mobile robot… 
2013
2013
The ordinary differential evolution (DE) algorithm employs real-valued vectors as genotypes. The author previously proposed an… 
2009
2009
This paper explores the idea that auto-teaching neural networks with evolved selfsupervision signals can lead to improved… 
2009
2009
This paper evaluates the Collective Neuro-Evolution (CONE) method, comparative to a related controller design method, in a… 
2008
2008
Studying the diversity of genetic algorithms is the most important topic to prevent the problem of premature convergence of the… 
2008
2008
For many complex Reinforcement Learning (RL) problems with large and continuous state spaces, neuroevolution has achieved… 
2002
2002
Neuroevolution techniques have been successful in many sequential decision tasks, such as robot control and game playing. This…