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GPEM
2006

Shortcomings with using edge encodings to represent graph structures

13 years 9 months ago
Shortcomings with using edge encodings to represent graph structures
There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we analyze edge encodings and show that they produce graphs with a node creation order connectivity bias (NCOCB). Additionally, depending on how input/output (I/O) nodes are handled, it can be difficult to generate ANNs with the correct number of I/O nodes. We compare two edge encoding languages, one which explicitly creates I/O nodes and one which connects to pre-existing I/O nodes with parameterized connection operators. Results from experiments show that these parameterized operators greatly improve the probability of creating and maintaining networks with the correct number of I/O nodes, remove the connectivity bias with I/O nodes and produce better ANNs. These results suggest that evolution with a representation which does not have the NCOCB will produce better performing ANNs. Finally we close with a discussion ...
Gregory Hornby
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where GPEM
Authors Gregory Hornby
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