Application of improved genetic algorithm in the evaluation system of enterprise

In this paper, in order to solve the problem of intelligent test paper, the author puts forward the improvement on chromosome coding, mutation algorithm and genetic operators of genetic algorithm after the study of genetic algorithm in theory and then proposes a multi-objective function optimization...

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Bibliographic Details
Published in:2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) pp. 1 - 4
Main Authors: Han Xiao-bing, Tian Yu-tong
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2015
Subjects:
Algorithm design and analysis
Artificial intelligence
Biological cells
chromosome coding
Encoding
Genetic algorithms
intelligent test paper
multi-objective function optimization
Sociology
Statistics
The improved genetic algorithm
ISBN:1479989185, 9781479989188
Online Access:Get full text
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Summary:In this paper, in order to solve the problem of intelligent test paper, the author puts forward the improvement on chromosome coding, mutation algorithm and genetic operators of genetic algorithm after the study of genetic algorithm in theory and then proposes a multi-objective function optimization algorithm. By experimental simulation, it is concluded that, compared with the ordinary genetic algorithm, the average fitness value of the improved algorithm has increased, moreover the average number of iterations and the time consumption has reduced. What's more, when the improved algorithm is applied to the enterprise appraisal, it is proved by experiments that the advantage of low repetition rate to realize the intelligent test paper, the success rate of test paper is 100%, and the repetition rate is 0.9%. Thus the superiority of the improved algorithm is reflected very well.
ISBN:1479989185
9781479989188
DOI:10.1109/ICSPCC.2015.7338838