OPTIMIZATION CONTROL OF E-BUSINESS INCOME BASING ON INTERVAL GENETIC ALGORITHM OF MULTI-OBJECTIVE

Qin Liao, Zhonghua Tang

2007

Abstract

In order to control the E-Business income, the relationship between 9 influencing factors and 3 controlling objectives is built by neural network. Then an interval genetic algorithm of multi-objective (IGAMO) is proposed to obtain the satisfactory interval solution instead of a single point solution provided by traditional algorithm. The IGAMO is constructed with 2-step genetic algorithm to find expending intervals satisfying the multiple income objectives, thus we gain the control conditions for influencing factors to make the income indexes fall in the anticipant intervals. The optimal control of E-Business income is proved to be feasible according to the analysis of the data collected from example e-Business enterprises of Guangzhou.

References

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Paper Citation


in Harvard Style

Liao Q. and Tang Z. (2007). OPTIMIZATION CONTROL OF E-BUSINESS INCOME BASING ON INTERVAL GENETIC ALGORITHM OF MULTI-OBJECTIVE . In Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST, ISBN 978-972-8865-79-5, pages 200-203. DOI: 10.5220/0001264502000203


in Bibtex Style

@conference{webist07,
author={Qin Liao and Zhonghua Tang},
title={OPTIMIZATION CONTROL OF E-BUSINESS INCOME BASING ON INTERVAL GENETIC ALGORITHM OF MULTI-OBJECTIVE},
booktitle={Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,},
year={2007},
pages={200-203},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001264502000203},
isbn={978-972-8865-79-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,
TI - OPTIMIZATION CONTROL OF E-BUSINESS INCOME BASING ON INTERVAL GENETIC ALGORITHM OF MULTI-OBJECTIVE
SN - 978-972-8865-79-5
AU - Liao Q.
AU - Tang Z.
PY - 2007
SP - 200
EP - 203
DO - 10.5220/0001264502000203