Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Creation of Neural Networks Based on Developmental and Evolutionary Principles
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Proceedings of the European Conference on Genetic Programming
Networks analysis, complexity, and brain function
Complexity - Special issue: Selection, tinkering, and emergence in complex networks
A Taxonomy for artificial embryogeny
Artificial Life
Towards an empirical measure of evolvability
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
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
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Picbreeder: evolving pictures collaboratively online
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st 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
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
HyperNEAT controlled robots learn how to drive on roads in simulated environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving the placement and density of neurons in the hyperneat substrate
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Investigating whether hyperNEAT produces modular neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Indirectly encoding neural plasticity as a pattern of local rules
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Enhancing es-hyperneat to evolve more complex regular neural networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Evolution of cartesian genetic programs for development of learning neural architecture
Evolutionary Computation
Quadtree-structured recursive plane decomposition coding of images
IEEE Transactions on Signal Processing
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
Evolving multimodal controllers with HyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. Although researchers in the field of neuroevolution NE attempt to recapitulate this process, artificial neural networks ANNs so far evolved through NE algorithms do not match the distinctive capabilities of biological brains. The recently introduced hypercube-based neuroevolution of augmenting topologies HyperNEAT approach narrowed this gap by demonstrating that the pattern of weights across the connectivity of an ANN can be generated as a function of its geometry, thereby allowing large ANNs to be evolved for high-dimensional problems. Yet the positions and number of the neurons connected through this approach must be decided a priori by the user and, unlike in living brains, cannot change during evolution. Evolvable-substrate HyperNEAT ES-HyperNEAT, introduced in this article, addresses this limitation by automatically deducing the node geometry from implicit information in the pattern of weights encoded by HyperNEAT, thereby avoiding the need to evolve explicit placement. This approach not only can evolve the location of every neuron in the network, but also can represent regions of varying density, which means resolution can increase holistically over evolution. ES-HyperNEAT is demonstrated through multi-task, maze navigation, and modular retina domains, revealing that the ANNs generated by this new approach assume natural properties such as neural topography and geometric regularity. Also importantly, ES-HyperNEAT's compact indirect encoding can be seeded to begin with a bias toward a desired class of ANN topographies, which facilitates the evolutionary search. The main conclusion is that ES-HyperNEAT significantly expands the scope of neural structures that evolution can discover.