Toward an estimation of distribution algorithm for the evolution of artificial neural networks

Authors:
Graham Holker;Marcus Vinicius dos Santos
Affiliations:
Ryerson University, Toronto, ON, Canada;Ryerson University, Toronto, ON, Canada
Venue:
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Year:
2010

Citing 3
Cited 1

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Abstract

This paper presents the preliminary results of a unique method of neuroevolution called Probabilistic Developmental Neuroevolution (PDNE). PDNE builds upon Gene Expression Programming (GEP) and Probabilistic Incremental Program Evolution (PIPE). Instead of building a Probabilistic Prototype Tree, as in PIPE, a Probabilistic Prototype Chromosome is built. The chromosome has a similar structure to a GEP chromosome (head, tail, and weight domain) and contains probabilities for each element of the gene. With this methodology, neural networks can be expressed in a similar manner to GEP, and solutions can be evolved via an Estimation of Distribution Algorithm. Preliminary results show promise, but further work is required to match the results of GEP.