Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Applying ESP and Region Specialists to Neuro-Evolution for Go
Applying ESP and Region Specialists to Neuro-Evolution for Go
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Modelling robotic cognitive mechanisms by hierarchical cooperative coevolution
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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Most of the recent neuroevolution (NE) approaches explore new network topologies based on a neuron-centered design principle. So far evolving connections has been poorly explored. In this paper, we propose a novel NE algorithm called Evolving Efficient Connections (EEC), where the connection weights and the connection paths of networks are evolved separately. We compare our new method with standard NE and several popular NE algorithms, SANE, ESP and NEAT. The experimental results indicate evolving connection weights along with connection paths can significantly enhance the performance of standard NE. Moreover the performances of cooperative coevolutionary algorithms are superior to non-cooperative evolutionary algorithms.