Towards Lifelong Learning in Optimisation Algorithms

Emma Hart

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 Harvard Style

Hart E. (2017). Towards Lifelong Learning in Optimisation Algorithms.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 7-9. DOI: 10.5220/0006810500010001


in Bibtex Style

@conference{ijcci17,
author={Emma Hart},
title={Towards Lifelong Learning in Optimisation Algorithms},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={7-9},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006810500010001},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Towards Lifelong Learning in Optimisation Algorithms
SN - 978-989-758-274-5
AU - Hart E.
PY - 2017
SP - 7
EP - 9
DO - 10.5220/0006810500010001