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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer, Singapore
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28029/1/Self-adaptive%20Population%20Size%20Strategy%20Based%20on%20Flower1.pdf |
| _version_ | 1825813279593725952 |
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| author | Abdullah, Nasser Kamal Z., Zamli |
| author_facet | Abdullah, Nasser Kamal Z., Zamli |
| author_sort | Abdullah, Nasser |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:41:38Z |
| format | Conference or Workshop Item |
| id | UMPir28029 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:41:38Z |
| publishDate | 2019 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | UMPir280292020-03-02T07:17:29Z 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 (Published) https://doi.org/10.1007/978-3-319-99007-1_23 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/28029/1/Self-adaptive%20Population%20Size%20Strategy%20Based%20on%20Flower1.pdf |
| work_keys_str_mv | AT abdullahnasser selfadaptivepopulationsizestrategybasedonflowerpollinationalgorithmfortwaytestsuitegeneration AT kamalzzamli selfadaptivepopulationsizestrategybasedonflowerpollinationalgorithmfortwaytestsuitegeneration |