Research on Intelligent Test Paper Generating System Based on Improved Genetic Algorithm

According to the characteristics of the online test paper composition, the genetic algorithm is preferred as the core algorithm. However, there are some problems in the traditional genetic algorithm when it is being used, such as low efficiency in the later search period and easy to fall into the local optimal solution. To solve this problem, this paper makes some optimizations for the traditional genetic algorithm's coding scheme, initial population, fitness function and genetic operator. After the improvement, the composing test paper module based on genetic algorithm was finally implemented in java language through seven parts.

Aimed for the problems in traditional genetic algorithm of low search efficiency and easily falling into the local optimal solution, an improved genetic algorithm is proposed in this paper. So as to save the storage space, the simple one-dimensional code method is adopted to replace the method of complex two-dimensional coding. In the design of genetic operators, many operations such as crossover and mutation are redefined to avoid getting into the local optimum. Then the two fitness functions of collision-free path and the shortest distance are fused into one for the following genetic optimization. In the case of the same population parameters, 100 trials are respectively developed with the method of improved genetic algorithm and traditional genetic algorithm. Among them, the improved genetic algorithm to search the optimal path gets to 95 times, and the shortest path is 20.9706. Besides, the average searching time takes up 217ms. While the number of traditional method to search for the optimal path reaches up to 62 times, the shortest path can be 25.0711, and the average searching time needs 345ms. So compared to the tests results referred above, the improved genetic algorithm is more efficient and can get a better solution than the traditional genetic algorithm.

Test paper generation is a multi-objective parameter optimization problem under multi-constraints. Aiming at the defects of test paper quality and speed in intelligent test paper generation, the genetic algorithm is improved on the basis of the original genetic algorithm. From the coding method of the test questions, the establishment of weighted objective function to optimize the fitness function, the selection of crossover operator, the setting of mutation, and the combination of optimization strategy and roulette to select the operation, the optimization design is carried out. It achieves good adaptability and can generate test paper quickly and successfully, which improves the efficiency of generating test paper. The simulation results show that, under the same constraints, the algorithm can generate test papers successfully, and improve the success rate and efficiency of generating test papers, and effectively optimize the problems in automatic generating test papers.

Intelligent test paper generation problem is a multi-objective parameter optimization problem under certain constraints, which has been realized by many algorithms. However, the existing intelligent test paper generation mostly adopts a single algorithm, and each algorithm has its own shortcomings. Sometimes it is inevitable to fall into the defects of the single algorithm in the process of test paper generation. Therefore, a mathematical model of intelligent test paper generation is proposed, which combines the advantages of artificial fish swarm algorithm and genetic algorithm to form a hybrid intelligent algorithm. At the beginning of intelligent test paper generation, the artificial fish swarm algorithm is used to quickly approach the test paper goal. In the process of test paper generation, when the optimal individual has no change or extremely small change in consecutive iterations, the genetic algorithm is used to jump the artificial fish individual to improve the convergence speed. The simulation results show that the hybrid intelligent algorithm can effectively optimize the effect of intelligent test paper generation by using a single algorithm alone.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

In order to solve the problem that the traditional genetic algorithm has a slow search speed and is easy to fall into the local optimal solution, a mathematical modeling method of an improved genetic algorithm in random power fluctuation is proposed. Drawing on the idea of a genetic algorithm (GA) and using the randomness and stability trend of cloud droplets of the normal cloud model, the author proposes a new genetic algorithm, cloud genetic algorithm (CGA). CGA is implemented by the Y-condition cloud generator of the normal cloud model to realize the cross operation, and the basic cloud generator realizes the mutation operation. Finally, the power function optimization experiment and the IIR digital filter optimization design are carried out, and the standard GA, NQGA, CAGA, and LARES algorithms are carried out compared. The experimental results show that the IIR digital filter designed by CGA has the smallest maximum ripple (Ap) in the passband, which is 0.342, and the largest minimum attenuation (As) in the stopband, which is 34.27. It can be observed that the overall performance is better. The validity of the algorithm is proved, and it has a certain reference and application value.

In order to solve the problems such as blindfold search, slower convergence, and sometimes unsuccessfully search in the present genetic algorithms used for intelligent test paper generation, this paper introduces an improved genetic algorithm for intelligent test paper generation. This algorithm generates optimized initial chromosome group and controlling crossing and variation recurring to test paper parameters and randomization. Consequently, it can avoid the problems of blind search and slower convergence resulted by complete randomization controlling adopted by the present genetic algorithms. At last, this paper proves the improved genetic algorithm can better solve the intelligent test paper generation problem compared to present genetic algorithms mentioned in algorithm analysis section of this paper.

Genetic algorithms (GAs) are considered as a global search approach for optimization problems. Through the proper evaluation strategy, the best can be found from the numerous genetic combinations. Although the GA operations do provide the opportunity to find the optimum solution, they may fail in some cases, especially when the length of a chromosome is very long. In this paper, a data mining-based GA is presented to efficiently improve the traditional GA (TGA). By analyzing support and confidence parameters, the important genes, called DNA, can be obtained. By adopting DNA extraction, it is possible that TGA will avoid stranding on a local optimum solution. Furthermore, the new GA operation, DNA implantation, was developed for providing potentially high quality genetic combinations to improve the performance of TGA. Experimental results in the area of digital watermarking show that our data mining-based GA successfully reduces the number of evolutionary iterations needed to find a solution.

With the wide application of online examinations in colleges and universities, intelligent test paper generation technology has become a hot topic. In this paper, we propose an improved genetic simulated annealing algorithm (GSA) and apply it to the test paper generation process to improve the quality and speed of intelligent test paper generation. Firstly, we optimize the genetic operators in real number coding and introduce an adaptive adjustment strategy on the premise of maintaining individual diversity. Secondly, we apply simulated annealing to individuals of each generation. Finally, we conduct abundant experiments. The results show that the improved algorithm can promote the success rate of test paper generation and effectively enhance the quality of test paper.

Gene coding has always been an important and difficult problem in the course scheduling of genetic algorithm. Starting from the definition of the set and the limited conditions of the scheduling problem, this paper expounds the coding method of the scheduling problem of genetic algorithm in detail. The gene coding method in this paper is a decimal coding with special significance. The information of each course: teaching class, class time, course name, teaching teacher, classroom type and other information are encoded as chromosomes, and the coding method is more standardized and accurate. The crossover rate and variation rate of the designed genetic algorithm will automatically change with the change of fitness, and the self-adapting crossover rate and variation rate can make the algorithm fleetly get the globing greatest result. The fitness function is improved, and the traditional genetic algorithm is compared with the improved genetic algorithm. From the final results, It shows that the improved genetic algorithm is more excellent in efficiency and performance.

A new genetic algorithm (GA), called intelligent GA, is proposed by the inclusion of a fractional factorial design in the crossover operator to determine the best combination of design variables. That is, in the proposed GA, the chromosomes of children are generated via an intelligent crossover process with factorial experiments after completing the selection of GA operators. This gene mating process differs from the traditional GA, where the child chromosome is generally generated via randomization. Accordingly, the traditional GA incorporating the technique of fractional factorial design into the crossover operator markedly enhances the performance in terms of evolutionary efficiency. Another GA, called Taguchi-GA, that incorporates the Taguchi orthogonal arrays into the crossover operator is also developed to compare the performance of the presented intelligent GA. Four representative test cases, including two multimodal functions, a nonlinear dynamic control function, and a profile fitting of a high lift airfoil are performed via the traditional GA, micro-GA, Taguchi-GA, and present GA to assess the capacity and efficiency of the proposed GA. The computational solutions of the convergence history and optimal objective function for the test cases demonstrate that the proposed intelligent GA outperforms the other three GAs. Moreover, the presented new intelligent GA and the other three GAs are applied to the aerodynamic optimization of two airplanes, namely, an advanced defense airplane and an F-16A fighter. The redesigned wing planform is obtained by computations made with an aerodynamic flow solver, and the present GA depicts the optimal swept angle, aspect ratio, and taper ratio. The drag distribution and convergence history show that the proposed intelligent GA has a high evolutionary efficiency when applied to practical engineering problems.

In order to improve the efficiency and reduce energy consumption of unmanned sailing boats for water quality inspection and sampling, particle swarm optimization was used for global path planning. The traditional particle swarm optimization algorithm has the problem of premature convergence because it is easy to fall into the local optimal solution. To solve this problem, an improved particle swarm optimization algorithm is proposed. Based on the traditional particle swarm optimization algorithm, the crossover and mutation operations in genetic algorithm are introduced. Firstly, the particle swarm was initialized and the fitness of the particle was calculated, and the individual optimal value and local optimal value were updated. Then crossover and mutation operations are introduced to update the particle velocity and position according to the results. Finally, the convergence of the algorithm is judged according to the decision criteria. The simulation results show that compared with the traditional particle swarm optimization algorithm, this algorithm can effectively avoid the algorithm falling into the local optimal solution, and greatly shorten the overall path length and reduce path planning time.

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Application of a hybrid PSO-GA optimization algorithm in determining pyrolysis kinetics of biomass

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.