Automatic definition of modular neural networks
Adaptive Behavior
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The sensitivity of HyperNEAT to different geometric representations of a problem
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
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
HybrID: a hybridization of indirect and direct encodings for evolutionary computation
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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
A novel generative encoding for evolving modular, regular and scalable networks
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
A cell-based developmental model to generate robot morphologies
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a computationally efficient high-level abstraction of development. This class of algorithms is intended to provide many of the desirable properties produced in biological phenotypes by natural developmental processes, such as regularity, modularity and hierarchy. While it has been previously shown that HyperNEAT produces regular artificial neural network (ANN) phenotypes, in this paper we investigated the open question of whether HyperNEAT can produce modular ANNs. We conducted such research on problems where modularity should be beneficial, and found that HyperNEAT failed to generate modular ANNs. We then imposed modularity on HyperNEAT's phenotypes and its performance improved, demonstrating that modularity increases performance on this problem. We next tested two techniques to encourage modularity in HyperNEAT, but did not observe an increase in either modularity or performance. Finally, we conducted tests on a simpler problem that requires modularity and found that HyperNEAT was able to rapidly produce modular solutions that solved the problem. We therefore present the first documented case of HyperNEAT producing a modular phenotype, but our inability to encourage modularity on harder problems where modularity would have been beneficial suggests that more work is needed to increase the likelihood that HyperNEAT and similar algorithms produce modular ANNs in response to challenging, decomposable problems.