Evolving neural networks through augmenting topologies
Evolutionary Computation
Adding Continuous Components to L-Systems
L Systems, Most of the papers were presented at a conference in Aarhus, Denmark
Networks analysis, complexity, and brain function
Complexity - Special issue: Selection, tinkering, and emergence in complex networks
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
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Generative and developmental systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Phenotypic, developmental and computational resources: scaling in artificial development
Proceedings of the 10th annual conference on Genetic and evolutionary computation
How generative encodings fare on less regular problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Tree-Based Indirect Encodings for Evolutionary Development of Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
How a Generative Encoding Fares as Problem-Regularity Decreases
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
When and why development is needed: generative and developmental systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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
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 modular neural-networks through exaptation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Scalable Neural Networks for Board Games
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Combining Multiple Inputs in HyperNEAT Mobile Agent Controller
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
NEAT in HyperNEAT substituted with genetic programming
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Autonomous Agents and Multi-Agent Systems
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
NEATfields: evolution of neural fields
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Evolving a single scalable controller for an octopus arm with a variable number of segments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Evolving neural networks for geometric game-tree pruning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the correlations between developmental diversity and genomic composition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
DXNN: evolving complex organisms in complex environments using a novel tweann system
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Distance measures for HyperGP with fitness sharing
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Efficient neuroevolution for a quadruped robot
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolving large-scale neural networks for vision-based reinforcement learning
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A multi-objective approach to evolving platooning strategies in intelligent transportation systems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Connectivity patterns in biological brains exhibit many repeating motifs. This repetition mirrors inherent geometric regularities in the physical world. For example, stimuli that excite adjacent locations on the retina map to neurons that are similarly adjacent in the visual cortex. That way, neural connectivity can exploit geometric locality in the outside world by employing local connections in the brain. If such regularities could be discovered by methods that evolve artificial neural networks (ANNs), then they could be similarly exploited to solve problems that would otherwise require optimizing too many dimensions to solve. This paper introduces such a method, called Hypercube-based Neuroevolution of Augmenting Topologies (HyperNEAT), which evolves a novel generative encoding called connective Compositional Pattern Producing Networks (connective CPPNs) to discover geometric regularities in the task domain. Connective CPPNs encode connectivity patterns as concepts that are independent of the number of inputs or outputs, allowing functional large-scale neural networks to be evolved. In this paper, this approach is tested in a simple visual task for which it effectively discovers the correct underlying regularity, allowing the solution to both generalize and scale without loss of function to an ANN of over eight million connections.