Genetic algorithm intelligent test paper composition method and device based on segmented real number coding and readable storage medium

Abstract

The invention provides a genetic algorithm intelligent test paper composition method and device based on segmented real number coding and a readable storage medium, and the method comprises the steps:1, performing test question coding: mapping each set of test paper into an independent chromosome in a genetic algorithm, mapping test questions in the test paper into genes, wherein gene values needto be completed under the support of a coding mechanism; 2, generating an initial group, and adopting a random extraction mode to generate N sets of test papers, namely an initial population; and 3,calculating individual fitness, calculating fitness values of all individuals in the initial population obtained through initialization, and carrying out quality evaluation; 4, selecting operators, eliminating inferior individuals according to the fitness values, and reserving excellent individuals in the population; 5, generating a new operator, wherein the new operator is generated in a gene crossing and mutation mode; and 6, setting a termination condition, searching test questions matched with characteristic parameters in a test question bank according to a given constraint condition so asto extract the optimal test question combination, and selecting an individual with the highest fitness value as a final test paper.

Description

technical field [0001] The invention relates to the field of genetic algorithms, in particular to a method for intelligently forming papers based on segmented real number coding of genetic algorithms, equipment and a readable storage medium. Background technique [0002] With the popularization of computer use, paperless and automated examinations have gradually become a trend. One of the key steps to realize paperless examinations is intelligent composition of papers. Intelligent paper composition means that according to the requirements of the question maker, the test questions are extracted from the question bank, and a high-quality test paper that meets multiple constraints is automatically composed. [0003] Currently, there are three commonly used test-taking strategies: random test-taking algorithm, backtracking test-taking algorithm and genetic algorithm. Random paper formation is the most commonly used strategy. According to the control index of the state space, th...

AI Technical Summary

Problems solved by technology

Random paper formation is the most commonly used strategy. According to the control index of the state space, the test questions are randomly selected and added to the test paper, and repeated until the paper formation is completed, or the test questions that meet the conditions cannot be screened. Due to the large randomness and uncertainty, the test results are not ideal; the backtracking algorithm is based on the random algorithm, and records each state generated by the random algorithm. When the search fails, backtracking is performed, and the last recorded state is released, and then Then generate a new state type trial according to the law, through continuous backtracking trials until the test paper is generated or back to the initial state, the backtracking algorithm is more efficient for the test paper composition model with fewer constraints, but the memory usage The volume is large, the program structure is relatively complicated, and the time for forming papers is long, which makes it difficult to meet the real-time needs of users; compared with the previous two methods, the genetic algorithm has incomparable advantages in the speed and quality of paper making The field of paper composition has attracted much attention

Images