Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
How a Generative Encoding Fares as Problem-Regularity Decreases
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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
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
NEAT in HyperNEAT substituted with genetic programming
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Investigating whether hyperNEAT produces modular neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving policy geometry for scalable multiagent learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
On the deleterious effects of a priori objectives on evolution and representation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Critical factors in the performance of novelty search
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
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
Single-unit pattern generators for quadruped locomotion
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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HyperNEAT is a popular indirect encoding method for evolutionary computation that has performed well on a number of benchmark tasks. This paper presents a series of experiments designed to examine the critical factors for its success. First, we determine the fewest hidden nodes a genotypic network needs to solve several of these tasks. Our results show that all of these tasks are easy: they can be solved with at most one hidden node and require generating only trivial regular patterns. Then, we examine how HyperNEAT performs when the tasks are made harder. Our results show that HyperNEAT's performance decays quickly: it fails to solve all variants of these tasks that require more complex solutions. Next, we examine the hypothesis that fracture in the problem space, known to be challenging for regular NEAT, is even more so for HyperNEAT. Our results suggest that quite complex networks are needed to cope with fracture and HyperNEAT can have difficulty discovering them. Finally, we connect these results to previous experiments showing that HyperNEAT's performance decreases on irregular tasks. Our results suggest irregularity is an extreme form of fracture and that HyperNEAT's limitations are thus more severe than those experiments suggested.