Genetic algorithms can be used to generate input data in a real-time system that produce the worst-case execution time of a task. While generating the test data, the fitness function is normally evaluated using a cycle-accurate simulator of the processor architecture, which consumes a significant computational effort and time. We propose to replace the simulator-based actual execution with a predictive model that is trained using the samples acquired on the simulator. The feasibility of this proposal was evaluated using four distinct predictive models, namely artificial neural networks, generalized linear regression, gaussian process regression and support vector regression. The results obtained on the four benchmarks Bubble sort, Insertion Sort, Gnome sort and Shaker sorts indicate that the proposed use of prediction models can significantly reduce the temporal verification time. The time gain achieved is up to 17.7 times and the best accuracy achieved is 98.5%.

中文翻译：

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使用预测模型估计 WCET 以计算遗传算法的适应度函数

遗传算法可用于在实时系统中生成输入数据，从而产生任务的最坏情况执行时间。在生成测试数据时，通常使用处理器架构的周期精确模拟器评估适应度函数，这会消耗大量的计算工作和时间。我们建议将基于模拟器的实际执行替换为使用模拟器上获取的样本进行训练的预测模型。使用四种不同的预测模型评估了该提议的可行性，即人工神经网络、广义线性回归、高斯过程回归和支持向量回归。在四个基准上得到的结果冒泡排序、插入排序、Gnome 排序和 Shaker 排序表明，建议使用预测模型可以显着减少时间验证时间。实现的时间增益高达 17.7 倍，实现的最佳精度为 98.5%。