Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation

The performance of meta-heuristic algorithms is highly dependents on the fine balance between intensification and diversification. Too much intensification may result in the quick loss of diversity and aggressive diversification may lead to inefficient search. Therefore, there is a need for proper p...

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Main Authors:	Abdullah, Nasser, Kamal Z., Zamli
Format:	Conference or Workshop Item
Language:	English
Published:	Springer, Singapore 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/28029/ http://umpir.ump.edu.my/id/eprint/28029/ http://umpir.ump.edu.my/id/eprint/28029/1/Self-adaptive%20Population%20Size%20Strategy%20Based%20on%20Flower1.pdf

id	ump-28029
recordtype	eprints
spelling	ump-280292020-03-02T07:17:29Z http://umpir.ump.edu.my/id/eprint/28029/ Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation Abdullah, Nasser Kamal Z., Zamli QA75 Electronic computers. Computer science The performance of meta-heuristic algorithms is highly dependents on the fine balance between intensification and diversification. Too much intensification may result in the quick loss of diversity and aggressive diversification may lead to inefficient search. Therefore, there is a need for proper parameter controls to balance out between intensification and diversification. The challenge here is to find the best values for the control parameters to achieve acceptable results. Many studies focus on tuning of the control-parameters and ignore the common parameter, that is, the population size. Addressing this issue, this paper proposes self-adaptive population size strategy based on Flower Pollination Algorithm, called saFPA for t-way test suite generation. In the proposed algorithm, the population size of FPA is dynamically varied based on the current need of the search process. Experimental results show that saFPA produces very competitive results as compared to existing strategies. Springer, Singapore 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28029/1/Self-adaptive%20Population%20Size%20Strategy%20Based%20on%20Flower1.pdf Abdullah, Nasser and Kamal Z., Zamli (2019) Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation. In: Recent Trends in Data Science and Soft Computing: Proceedings of the 3rd International Conference of Reliable Information and Communication Technology (IRICT2018), 23-24 July 2018 , Kuala Lumpur, Malaysia. pp. 240-248., 843. ISBN 978-3-319-99007-1 https://doi.org/10.1007/978-3-319-99007-1_23
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abdullah, Nasser Kamal Z., Zamli Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
description	The performance of meta-heuristic algorithms is highly dependents on the fine balance between intensification and diversification. Too much intensification may result in the quick loss of diversity and aggressive diversification may lead to inefficient search. Therefore, there is a need for proper parameter controls to balance out between intensification and diversification. The challenge here is to find the best values for the control parameters to achieve acceptable results. Many studies focus on tuning of the control-parameters and ignore the common parameter, that is, the population size. Addressing this issue, this paper proposes self-adaptive population size strategy based on Flower Pollination Algorithm, called saFPA for t-way test suite generation. In the proposed algorithm, the population size of FPA is dynamically varied based on the current need of the search process. Experimental results show that saFPA produces very competitive results as compared to existing strategies.
format	Conference or Workshop Item
author	Abdullah, Nasser Kamal Z., Zamli
author_facet	Abdullah, Nasser Kamal Z., Zamli
author_sort	Abdullah, Nasser
title	Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
title_short	Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
title_full	Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
title_fullStr	Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
title_full_unstemmed	Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation
title_sort	self-adaptive population size strategy based on flower pollination algorithm for t-way test suite generation
publisher	Springer, Singapore
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/28029/ http://umpir.ump.edu.my/id/eprint/28029/ http://umpir.ump.edu.my/id/eprint/28029/1/Self-adaptive%20Population%20Size%20Strategy%20Based%20on%20Flower1.pdf
first_indexed	2023-09-18T22:43:58Z
last_indexed	2023-09-18T22:43:58Z
_version_	1777417101053526016

Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation

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