A Clustering-based Visual Analysis Tool for Genetic Algorithm

Habib Daneshpajouh, Nordin Zakaria

Abstract

While Genetic Algorithm (GA) is a powerful tool for combinatorial optimization, the vast population of candidate solutions it typically deploys and algorithm’s intrinsic randomness lead to difficulty in understanding its search behavior. We discuss in this paper a clustering-based visualization tool for GA that attempts to mediate this problem. GA population across its entire generations are clustered, and each cluster and its individuals are mapped to a visual symbol. The tool enables a GA researcher or user to understand better the behavior of a GA run, specifically the local searches it performs in its global exploration to go from one generation to another.

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Paper Citation


in Harvard Style

Daneshpajouh H. and Zakaria N. (2017). A Clustering-based Visual Analysis Tool for Genetic Algorithm . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 233-240. DOI: 10.5220/0006135902330240


in Bibtex Style

@conference{ivapp17,
author={Habib Daneshpajouh and Nordin Zakaria},
title={A Clustering-based Visual Analysis Tool for Genetic Algorithm},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006135902330240},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - A Clustering-based Visual Analysis Tool for Genetic Algorithm
SN - 978-989-758-228-8
AU - Daneshpajouh H.
AU - Zakaria N.
PY - 2017
SP - 233
EP - 240
DO - 10.5220/0006135902330240