Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
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
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
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
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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It has been shown that generative representations, which allow the reuse of code, perform well on problems with high regularity (i.e. where a phenotypic motif must be repeated many times). To date, however, generative representations have not been tested on irregular problems. It is unknown how they will fare on problems with intermediate and low amounts of regularity. This paper compares a generative representation to a direct representation on problems that range from having multiple types of regularity to one that is completely irregular. As the regularity of the problem decreases, the performance of the generative representation degrades to, and then underperforms, the direct encoding. The degradation is not linear, however, yet tends to be consistent for different types of problem regularity. Furthermore, if the regularity of each type is sufficiently high, the generative encoding can simultaneously exploit different types of regularities.