Comparing Small Population Genetic Algorithms over Changing Landscapes

Michael Curley, Seamus Hill

Abstract

This paper examines the performance and adaptability of a number of small population Genetic Algorithms (GAs) over a selection of dynamic landscapes. Much of the research in this area tends to focus on GA with relatively large populations for problem optimisation. However there is research, which suggests that GAs with smaller populations can also be effective over changing landscapes. This research compares the performance and adaptability of a number of these small population GA over changing landscapes. With small population GAs, convergence can occur quickly, which in turn affects the adaptability of a GA over dynamic landscapes. In this paper five GA variants using small population sizes are run over well-known unimodal and multimodal problems, which were tailored to produce dynamic landscapes. Adaptability within the population is considered a desirable feature for a GA to optimise a changing landscape and different methods are used to maintain a level of diversity within a population to avoid the problem of premature convergence, thereby allowing the GA population adapt to the dynamic nature of the search space. Initial results indicate that small population GAs can perform well in searching changing landscapes, with GAs which possess the ability to maintain diversity within the population, outperforming those that do not.

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Paper Citation


in Harvard Style

Curley M. and Hill S. (2017). Comparing Small Population Genetic Algorithms over Changing Landscapes.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 239-246. DOI: 10.5220/0006497802390246


in Bibtex Style

@conference{ijcci17,
author={Michael Curley and Seamus Hill},
title={Comparing Small Population Genetic Algorithms over Changing Landscapes},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={239-246},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006497802390246},
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 - Comparing Small Population Genetic Algorithms over Changing Landscapes
SN - 978-989-758-274-5
AU - Curley M.
AU - Hill S.
PY - 2017
SP - 239
EP - 246
DO - 10.5220/0006497802390246