Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
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
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Generative representations allow the reuse of code and thus facilitate the evolution of repeated phenotypic themes or modules. It has been shown that generative representations perform well on highly regular problems. To date, however, generative representations have not been tested on irregular problems. It is unknown how fast their performance degrades as the regularity of the problem decreases. In this report, we test a generative representation on a problem where we can scale a type of regularity in the problem. The generative representation outperforms a direct encoding control when the regularity of the problem is high but degrades to, and then underperforms, the direct control as the regularity of the problem decreases. Importantly, this decrease is not linear. The boost provided by the generative encoding is only significant for very high levels of regularity.