Automatic test paper generation method for network examination

Abstract

The present invention discloses an automatic test paper generation method for network examination. The method comprises: (1) setting a test paper generation constraint condition; (2) generating an initial group P0 according to the constraint condition; (3) calculating the fitness function value of each individual; (4) determining whether the individual fitness function satisfies the test paper generation condition or not, and if individual fitness function satisfies the test paper generation condition, generating the test and ending the automatic test paper generation method; if the individual fitness function does not satisfy the test paper generation condition, determining whether the individual fitness function reaches the maximum iterations or not; if the individual fitness function reaches the maximum iterations, adding 1 to the iterations, and performing the genetic manipulation; and (5) generating a new group P1 through the genetic manipulation, and returning back to the operation (3). The automatic test paper generation method for the network examination employs the genetic algorithm to complete the automatic test paper generation to rapidly obtain a scientific test, solve the problem that the examination system is large in test randomness and unstable in the test paper generation process and improve the examination fairness and rationality.

Description

technical field [0001] The invention relates to a method for forming papers, in particular to an automatic method for forming papers for network examinations. Background technique [0002] At present, most of the various examinations in schools and society adopt traditional examination methods. In this way, organizing a complete examination must go through at least the following four steps: manual production of papers, organization of examinations, manual marking of papers, score statistics and paper analysis. With the continuous increase of various types of examinations and the increasing number of candidates, the workload of organizing similar examinations will become larger and the efficiency will be lower and lower. The trend and method to solve the traditional examination method is to use computing technology and network technology to form a new examination method, that is, network examination. A current development trend is to adopt the computer-based online examinat...

AI Technical Summary

Problems solved by technology

[0010] 1. Improper use of operators in the genetic algorithm such as selection, crossover and mutation operators will lead to premature convergence, and improper selection of corresponding control parameters will also lead to premature convergence

[0011] 2. Limited group size

Because the population size is limited, the search optimal solution space is also limited, and inbreeding is easy, so that the population model quickly tends to a single state, thus falling into a local optimal solution

[0012] 3. The loss of patterns in the population is also a phenomenon of premature convergence of genetic algorithms

If it is not controlled, let the population randomly and continuously perform selection, crossover and mutation operations, which may lead to the loss of effective genes in the population

If the effective gene is lost to a certain extent, it will cause the algorithm to stop locally and cannot be effectively evolved.

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