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2012•Journal of Sanming UniversityRequires access

A Study of the Automatic Test System Based on Improved Genetic Algorithm

Dong Yuan

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Abstract

In view of the shortages,which the basic genetic algorithm in automatic test paper generation system is easy to fall into local optimal solution and iterative later easily premature convergence,the method of improving the initial population selection,the adaptive crossover probability and mutation probability of the improved genetic algorithm is proposed.The experimental results show that the improved genetic algorithm in the test effect and efficiency is better than the basic genetic algorithm.

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What this paper is about

In view of the shortages,which the basic genetic algorithm in automatic test paper generation system is easy to fall into local optimal solution and iterative later easily premature convergence,the method of improving the initial population selection,the adaptive crossover probability and mutation probability of the improved genetic algorithm is proposed.The experimental results show that the improved genetic algorithm in the test effect and efficiency is better than the basic genetic algorithm.

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Available abstract

In view of the shortages,which the basic genetic algorithm in automatic test paper generation system is easy to fall into local optimal solution and iterative later easily premature convergence,the method of improving the initial population selection,the adaptive crossover probability and mutation probability of the improved genetic algorithm is proposed.The experimental results show that the improved genetic algorithm in the test effect and efficiency is better than the basic genetic algorithm.

Key concepts: Crossover, Genetic algorithm, Premature convergence, Population-based incremental learning, Selection (genetic algorithm), Economic shortage, Mutation, Computer science

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