Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Neural Plasticity and Minimal Topologies for Reward-Based Learning
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
On the relationships between synaptic plasticity and generative systems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Evolving plastic neural networks for online learning: review and future directions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Toward nonlinear local reinforcement learning rules through neuroevolution
Neural Computation
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Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper aims to show that learning rules can be effectively indirectly encoded by extending the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method. Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution.