智能技术学报

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稿件标题: A Multi-Objective Evolutionary Approach to Selecting Security Solutions
稿件作者: Yunghee Lee, Tae Jong Choi, Chang Wook Ahn
关键字词: security; evolutionary algorithm; multi-objective genetic algorithm;arti cial intelligence
文章摘要: In many company or organization, owners want to deploy the most efficient security solutions at a low cost. In this paper, we propose a method of choosing the best security solution from various security solutions using multi-objective genetic algorithm considering cost and weakness-decrease. The proposed system can support the best security solutions in various aspects of security issues. We use the NSGA-II algorithm, which has been veri ed in a variety of elds, to provide a comparison with existing genetic algorithms. Our scheme has increased the dominant area by more than 30% compared to the previous scheme and can provide a more diverse solution set.
收录刊物: 2017年2卷2期
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