University of Sussex
Browse

Using novelty-biased GA to sample diversity in graphs satisfying constraints

Download (1.32 MB)
conference contribution
posted on 2023-06-09, 00:40 authored by Peter Overbury, Luc BerthouzeLuc Berthouze
The structure of the network underlying many complex systems, whether artificial or natural, plays a significant role in how these systems operate. As a result, much emphasis has been placed on accurately describing networks using network theoretic metrics. When it comes to generating networks with similar properties, however, the set of available techniques and properties that can be controlled for remains limited. Further, whilst it is becoming clear that some of the metrics currently used to control the generation of such networks are not very prescriptive so that networks could potentially exhibit very different higher-order structure within those constraints, network generating algorithms typically produce fairly contrived networks and lack mechanisms by which to systematically explore the space of network solutions. In this paper, we explore the potential of a multi-objective novelty-biased GA to provide a viable alternative to these algorithms. We believe our results provide the first proof of principle that (i) it is possible to use GAs to generate graphs satisfying set levels of key classical graph theoretic properties and (ii) it is possible to generate diverse solutions within these constraints. The paper is only a preliminary step, however, and we identify key avenues for further development.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Gecco Companion 2015

Publisher

ACM

Page range

1445-1446

Event name

2015 Annual Conference on Genetic and Evolutionary Computation

Event location

Madrid

Event type

conference

Event date

11-15th July 2015

Book title

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Place of publication

New York, NY

ISBN

9781450334884

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-03-30

First Open Access (FOA) Date

2017-08-08

First Compliant Deposit (FCD) Date

2016-03-30

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC