Aiming at the problem of low clinical practicability and accuracy in the clinical diagnosis of myocardial infarction， an auxiliary diagnosis method of myocardial infarction based on 12-lead ElectroCardioGram （ECG） signal was proposed. Firstly， denoising and data enhancement were performed on the 12-lead ECG signals. Secondly， aiming at the ECG signals of each lead， the statistical features including standard deviation， kurtosis coefficient and skewness coefficient were extracted respectively to reflect the morphological characteristics of ECG signals， meanwhile the entropy features including Shannon entropy， sample entropy， fuzzy entropy， approximate entropy and permutation entropy were extracted to characterize the time and frequency spectrum complexity， the new mode generation probability， the regularity and the unpredictability of the ECG signal time series as well as detect the small changes of ECG signals. Thirdly， the statistical features and entropy features of ECG signals were fused. Finally， based on the random forest algorithm， the performance of algorithm was analyzed and verified in both intra-patient and inter-patient modes， and the cross-validation technology was used to avoid over-fitting. Experimental results show that， the accuracy and F1 value of the proposed method in the intra-patient modes are 99.98% and 99.99% respectively， the accuracy and F1 value of the proposed method in the inter-patient mode are 94.56% and 97.05% respectively； and compared with the detection method based on single-lead ECG， the detection of myocardial infarction with 12-lead ECG is more logical for doctors’ clinical diagnosis.

In order to improve the performance of Differential Evolution (DE) algorithm and extend its adaptability for solving continuous optimization problems, an enhanced DE algorithm was proposed by using efficient local search under the Memetic framework. Specifically, based on the Davidon-Fletcher-Powell (DFP) method, an improved local search method named NDFP was put forward, which could speed up finding locally optimal solutions based on excellent individuals explored by the DE algorithm. Furthermore, a strategy on when and how to run the NDFP local search was also given, so as to strike a good balance between global search (i.e., DE) and local search (i.e., NDFP). The proposed strategy was also enhanced the adaptability of NDFP local search in the range of DE algorithm. To verify the efficiency of the proposed algorithm, extensive simulation experiments were conducted on up to 53 test functions from CEC2005 and CEC2013 Benchmarks. The experimental results show that, compared with DE/current-to-best/1, SaDE and EPSDE algorithms, the proposed algorithm can achieve better performance in terms of both precision and stability.

Focusing on nurse assignment problem, this paper firstly analyzed nurse assignment problem in aspects of patient-nurse relations, nurses’ professional titles, patients’ nursing grades. An improved stochastic programming model was built which was more suitable for hospitals in China. Then according to the solution structure of the problem, a Genetic Algorithm with Perturb Mutation (PMGA) which was added on every vectors among the solution with a probability was designed. Compared to random greedy algorithm and Bender’s decomposition based greedy algorithm in experiment, PMGA results were more effective than other methods in solving nurse assignment problem within 30 minutes and it would reduce workload more than 8.9% for each nurse in a shift. Especially, GA with perturb mutation was more efficient in solving multi-scenario, multi-trap nurse assignment problems which have solutions without field continuity.