Bet-based Evolutionary Algorithms: Self-improving Dynamics in Offspring Generation

Simon Reichhuber, Sven Tomforde

Abstract

Evolutionary Algorithms (EA) are a well-studied field in nature-inspired optimisation. Their success over the last decades has led to a large number of extensions, which are particularly suitable for certain characteristics of specific problems. Alternatively, variants of the basic approach have been proposed, for example to increase efficiency. In this paper, we focus on the latter: We propose to enrich the evolutionary problem with a self- controlling betting strategy to optimise the evolution of individuals over successive generations. For this purpose, each individual is given a betting parameter to be co-optimised, which allows him to improve his chances of “survival” by betting. We analyse the behaviour of our approach compared to standard procedures by using a reference set of complex functional problems.

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Paper Citation


in Harvard Style

Reichhuber S. and Tomforde S. (2021). Bet-based Evolutionary Algorithms: Self-improving Dynamics in Offspring Generation.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1192-1199. DOI: 10.5220/0010345611921199


in Bibtex Style

@conference{icaart21,
author={Simon Reichhuber and Sven Tomforde},
title={Bet-based Evolutionary Algorithms: Self-improving Dynamics in Offspring Generation},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1192-1199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010345611921199},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Bet-based Evolutionary Algorithms: Self-improving Dynamics in Offspring Generation
SN - 978-989-758-484-8
AU - Reichhuber S.
AU - Tomforde S.
PY - 2021
SP - 1192
EP - 1199
DO - 10.5220/0010345611921199