Title

Constraining Connectivity To Encourage Modularity In Hyperneat

Keywords

Artificial neural networks; Generative and developmental systems; HyperNEAT; Modularity

Abstract

A challenging goal of generative and developmental systems (GDS) is to effectively evolve neural networks as complex and capable as those found in nature. Two key properties of neural structures in nature are regularity and modularity. While HyperNEAT has proven capable of generating neural network connectivity patterns with regularities, its ability to evolve modularity remains in question. This paper investigates how altering the traditional approach to determining whether connections are expressed in HyperNEAT influences modularity. In particular, an extension is introduced called a Link Expression Output (HyperNEAT-LEO) that allows HyperNEAT to evolve the pattern of weights independently from the pattern of connection expression. Because HyperNEAT evolves such patterns as functions of geometry, important general topographic principles for organizing connectivity can be seeded into the initial population. For example, a key topographic concept in nature that encourages modularity is locality, that is, components of a module are located near each other. As experiments in this paper show, by seeding HyperNEAT with a bias towards local connectivity implemented through the LEO, modular structures arise naturally. Thus this paper provides an important clue to how an indirect encoding of network structure can be encouraged to evolve modularity. Copyright 2011 ACM.

Publication Date

8-24-2011

Publication Title

Genetic and Evolutionary Computation Conference, GECCO'11

Number of Pages

1483-1490

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2001576.2001776

Socpus ID

84860391193 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84860391193

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