Towards Lifelong Learning in Optimisation Algorithms

Emma Hart

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Paper

Towards Lifelong Learning in Optimisation Algorithms

In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 0IJCCI, 7-9, 2017 , Funchal, Madeira, Portugal

Author: Emma Hart

Affiliation: Edinburgh Napier University, United Kingdom

Keyword(s): Optimisation, Lifelong Learning.

Abstract: Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to improve despite being exposed to a wealth of further example instances. Furthermore, if the characteristics of the instances being solved shift over time, the tuned algorithm is likely to perform poorly. To counter this, we propose the lifelong learning optimiser, which autonomously and continually refines its optimisation algorithm(s) to improve with experience, and generates novel algorithms if performance drops. The approach combines genetic programming with an autonomous management method inspired by the operation of the natural immune system.

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Paper citation in several formats:

Hart, E. (2017). Towards Lifelong Learning in Optimisation Algorithms. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 7-9. DOI: 10.5220/0006810500010001

@conference{ijcci17,
author={Emma Hart.},
title={Towards Lifelong Learning in Optimisation Algorithms},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI},
year={2017},
pages={7-9},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006810500010001},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - Towards Lifelong Learning in Optimisation Algorithms
SN - 978-989-758-274-5
IS - 2184-3236
AU - Hart, E.
PY - 2017
SP - 7
EP - 9
DO - 10.5220/0006810500010001
PB - SciTePress