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Author: Samreen Umer

Affiliation: University College Cork, Ireland

ISBN: 978-989-758-157-1

Keyword(s): Microbial Genetic Algorithm, Evolutionary Algorithms, Mutation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Microbial Genetic Algorithm (MGA) is a simple variant of genetic algorithm and is inspired by bacterial conjugation for evolution. In this paper we have discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator. We also proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity in population in a cost effective way. Our investigation suggests that a clever choice of mutation operator can enhance the performance of basic MGA significantly.

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Paper citation in several formats:
Umer, S. (2015). Investigation into Mutation Operators for Microbial Genetic Algorithm.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 299-305. DOI: 10.5220/0005614902990305

@conference{ecta15,
author={Samreen Umer.},
title={Investigation into Mutation Operators for Microbial Genetic Algorithm},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={299-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005614902990305},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Investigation into Mutation Operators for Microbial Genetic Algorithm
SN - 978-989-758-157-1
AU - Umer, S.
PY - 2015
SP - 299
EP - 305
DO - 10.5220/0005614902990305

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