Abstract:Sampling inspection uses the sample characteristics to estimate that of the population, and it is an important method to describe the population, which has the features of low cost, strong applicability and high scientificity. This paper aims at the sampling inspection of the master's degree thesis to ensure their quality, which is commonly estimated by random sampling. Since there are disadvantages in random sampling, a hybrid algorithm combined with an improved genetic algorithm and a simulated annealing algorithm is proposed in this paper. Furthermore, a novel mutation strategy is introduced according to the specialty of Shanghai's thesis sampling to improve the efficiency of sampling inspection; the acceleration of convergence of the algorithm can also take advantage of this. The new algorithm features the traditional genetic algorithm, and it can obtain the global optimum in the optimization process and provide the fairest sampling plan under the constraint of multiple sampling indexes. The experimental results on the master's thesis dataset of Shanghai show that the proposed algorithm well meets the requirements of the sampling inspection in Shanghai with a lower time-complexity.
Real-time software systems with tight performance requirements are abundant. These systems frequently use many different algorithms and if any one of these algorithms was to experience behavior that is atypical because of the input, the entire system may not be able to meet its performance requirements. Unfortunately, it is algorithmically intractable, if not unsolvable, to find the inputs which would cause worst-case behavior. If inputs can be identified that make the system take, say, ten times longer compared to the time it usually takes, that information is valuable for some systems. In this paper, we present a method for finding inputs that perform much worse than the average input to different algorithms. We use the simulated annealing heuristic search method and show that this method is successful in finding worst-case inputs to several sorting algorithms, using several measures of an algorithm's runtime.
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