## 2 Background  
### Literature Review  
According to the study, the performance of the classifier is affected greatly by its inner parameters and the features in the data. Metaheuristics have great effectiveness in solving this type of problem as shown in many works [28; 29; 30; 31; 32], such as object tracking [33; 34], the traveling salesman problem [35], gate resource allocation [36; 37], multi-attribute decision-making [38; 39], the design of the power electronic circuit [40; 41], fractional-order controllers [42], medical diagnoses [43; 44], big data optimization problems [45], green supplier selections [46], economic emission dispatch problems [47], scheduling problems [48; 49], and combination optimization problems [50]. This study proposes an enhanced crow search algorithm (CSA) [51] to simultaneously optimize the hyperparameters of the kernel extreme learning machine (KELM) and the feature space for predicting the entrepreneurial intention of college students. The CSA is a novel-inspired metaheuristic method proposed in 2016 and has resolved many complex optimization problems [51], with many researchers attempting to boost it from different aspects [52; 53; 54; 55; 56]. For example, [PERSON] et al. [57] proposed a hybrid particle swarm optimization with the CSA for feature selection, and the result of the proposed model gained an accuracy of 89.67% on 15 datasets. [PERSON] et al. [58] designed an improved CSA to optimize a hybrid renewable energy system in radial distribution networks, and the results showed significant active losses and voltage deviations. [PERSON] et al. [59] adopted the CSA for big data classification, and the results showed a higher classification performance compared with other algorithms. [PERSON] et al. [60] developed a cellular CSA with topological neighborhood shapes to resolve three real-world problems. [PERSON] et al. [61] designed a boosted CSA-based tournament selection strategy, and the results showed that the optimal determination of power exchange and the incentive rate could lead to decreasing operation costs. [PERSON] et al. [62] developed a binary CSA with a time-varying flight length to solve the feature selection problem, and the results showed that the proposed feature selection technique behaved better than other approaches. [PERSON] et al. [63] designed an enhanced CSA for a forgery detection technique in the image, and the results exhibited that the proposed classification performs better than most algorithms. [PERSON] et al. [64] used the CSA with chaotic mapping for fine-turning the controller and varied the efficacy of the controller in its frequency regulation, as validated. [PERSON] et al. [65] introduced a novel boosted CSA to predict [PERSON]'s disease with an accuracy of 100% and helped patients obtain proper treatment. [PERSON] et al. [66] developed a hybrid support vector regression and CSA to handle the multi-objective optimization of microalgae-based wastewater treatment. [PERSON] et al. [67] proposed an enhanced CSA to deal with energy optimization problems, and the results showed that the approach can better obtain proper solutions with lower calculation times. [PERSON] et al. [68] developed a novel crow spiral-based search algorithm to solve the design problem formulated, and the gained results confirmed the success of the filter design. [PERSON] et al. [69] designed a hybrid CSA with an arithmetic crossover to two real-world engineering optimization problems and gained effective results. [PERSON] et al. designed [70] an improved CSA with an extreme learning machine model to effectively forecast short-term wind power.