Abstract
The test task scheduling problem (TTSP) is an essential issue in automatic test system. In this paper, a new non-integrated algorithm called GASCR which combines a genetic algorithm with a new rule for scheme selection is adopted to find optimal solutions. GASCR is a hierarchal approach based on the characteristics of TTSP because the given problem can be decomposed into task sequence and scheme choice. GA with the non-Abelian (Nabel) crossover and stochastic tournament (ST) selector is used to find a proper task sequence. The problem-specific scheme choice rule addresses the scheme choice. To evaluate the proposed method, we apply it on several benchmarks and the results are compared with some well-known algorithms. The experimental results show the competitiveness of the GASCR for solving TTSP.
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This research is supported by the National Natural Science Foundation of China under Grant No. 61101153.
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Shi, J., Lu, H., Mao, K. (2016). Solving the Test Task Scheduling Problem with a Genetic Algorithm Based on the Scheme Choice Rule. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_3
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DOI: https://doi.org/10.1007/978-3-319-41009-8_3
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