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

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

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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.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Meta-heuristic; Flower Pollination Algorithm; Self-adaptive population size; T-way testing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 02 Mar 2020 07:17
Last Modified: 02 Mar 2020 07:17
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