Evolving neural networks through augmenting topologies
Evolutionary Computation
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Efficient evolution of neural network topologies
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Evolution of neural networks, as implemented in NEAT, has proven itself successful on a variety of low-level control problems such as pole balancing and vehicle control. Nonetheless, high-level control problems still seem to trouble neuroevolution approaches. This paper presents such a complex task and explores how different aspects of problem difficulty have varying strong influences on NEAT's performance. Based on these findings, the question is discussed why certain problem domains are less beneficial for neuroevolution approaches' performance, which may provide useful insights into how to design the next generation of neuroevolution algorithms.