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Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem

机译：测试任务调度问题中并行遗传算法的适应距离分析

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The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorithm, called Parallel Genetic Algorithm (PGA), is proposed to determine the optimal solutions. Two branches-the tasks sequence and scheme choice run the classic genetic algorithm independently and they balance each other due to their interaction in the given problem. To match the frame of the PGA, a vector group encoding method is provided. In addition, the fitness distance coefficient (FDC) is first applied as the measurable step of landscape to analyze TTSP and guide the design of PGA when solving the TTSP. The FDC is the director of the search space of the TTSP, and the search space determinates the performance of PGA. The FDC analysis shows that the TTSP owes a large number of local optima. Strong space search ability is needed to solve TTSP better. To make PGA more suitable to solve TTSP, three crossover and four selection operations are adopted to find the best combination. The experiments show that due to the characteristic of TTSP and the randomness of the algorithm, the PGA has a lowprobability for optimizing the TTSP, but PGAwith Nabel crossover and stochastic tournament selection performs best. The assumptions of FDC are consistent with the success rate of PGA when solving the TTSP. Communicated by G. Acampora.
机译：尽管自动测试系统应用范围很广，但由于自动测试系统的广泛应用，测试任务计划问题（TTSP）引起了越来越多的关注。 TTSP的主要特征是任务序列和方案选择之间的紧密交互。基于这一点，提出了遗传算法的并行植入，称为并行遗传算法（PGA），以确定最优解。两个分支-任务序列和方案选择独立运行经典的遗传算法，由于在给定问题中的相互作用，它们相互平衡。为了匹配PGA的帧，提供了向量组编码方法。另外，适应距离系数（FDC）首先被用作景观的可测量步骤，以分析TTSP，并在求解TTSP时指导PGA的设计。 FDC是TTSP搜索空间的主管，而搜索空间决定了PGA的性能。 FDC分析表明，TTSP拥有大量的局部最优值。为了更好地解决TTSP，需要强大的空间搜索能力。为了使PGA更适合于解决TTSP，采用了三个交叉和四个选择操作来找到最佳组合。实验表明，由于TTSP的特点和算法的随机性，PGA优化TTSP的概率较低，而具有Nabel交叉和随机锦标赛选择的PGA表现最佳。 FDC的假设与解决TTSP时PGA的成功率一致。由G. Acampora沟通。

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