Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning
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Summary
EANT, “Evolutionary Acquisition of Neural Topologies”, a method that creates neural networks (NNs) by evolutionary reinforcement learning, can create NNs that are very specialised and achieve a very good performance while being relatively small.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 0
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W100361683
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18720274
Keywords
Neuroevolution, Reinforcement learning, Network topology, Evolutionary acquisition of neural topologies, Artificial neural network
References
- Efficient reinforcement learning through Evolutionary Acquisition of Neural Topologies
- Introduction to stochastic search and optimization - estimation, simulation, and control
- Neural Networks - A Systematic Introduction
- Dynamic sensor-based control of robots with visual feedback
- Optimization by Simulated Annealing
- Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C.; 2003) [book review]
- Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
- Practical Methods of Optimization: Fletcher/Practical Methods of Optimization
- V. Adaptive Control Processes
- The Cascade-Correlation Learning Architecture
- Evolving Neural Networks through Augmenting Topologies
- Completely Derandomized Self-Adaptation in Evolution Strategies
- Learning Neural Networks for Visual Servoing Using Evolutionary Methods
- A new evolutionary system for evolving artificial neural networks
- Multilayer feedforward networks are universal approximators
- An evolutionary algorithm that constructs recurrent neural networks
- A tutorial on visual servo control
- Advances in Computational Intelligence
- Multilayer feedforward networks are universal approximators
- Introduction to Evolutionary Computing
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