基因表达式编程的改进及其在知识发现中的应用研究
发布时间：2018-12-16 10:32
【摘要】：基因表达式编程算法(Gene Expression Programming,GEP)是一种新型的处理高维的、不确定性因素的智能进化算法,它能够挖掘出隐藏在数据中的知识,如规则、模型等,并且不需要任何的先验知识。该算法以独特的编码方式、优秀的数据挖掘能力和高度非线性系统的处理能力吸引了许多国内外研究者的关注,并广泛应用于众多的实际领域中。本文主要的研究工作是对标准GEP算法进行改进,并将其应用到知识发现中的两大问题中,即麦蚜种群建模和建筑工程造价预测,可为麦蚜种群发生量的预测和建筑项目的可行性研究以及合理的设计方案提供依据。本研究的具体工作有以下几点:(1)在阅读大量文献和建模预测内容的基础上,本文对应用领域的重点知识进行总结概括,介绍了麦蚜种群建模的过程和现状以及建筑工程造价预测特征量的提取与分类;对基因表达式编程算法的原理和编码方式进行了概述,详细介绍了算法的流程和基本操作。(2)依据人工干预思想,本研究提出了一种由人工干预系统和自然进化系统组成的双系统协同进化的基因表达式编程算法(DSCE-GEP)。人工干预系统包括个体干预和种群干预,个体干预即采用优质基因库对种群中的个体进行增优和去劣操作,旨在提高个体的质量;种群干预则是利用信息熵通过引入随机个体和镜像个体来提高种群多样性。(3)针对提出的改进算法,本研究与类似算法进行了对比仿真实验,分析、验证了DSCE-GEP算法的有效性和先进性。同时,本文将DSCE-GEP算法应用于农业和建筑业中,对中国农业科学院提供的麦蚜种群数据进行建模,并对文献中列举的建筑工程项目数据进行建模预测。实验结果表明,本研究构建的基于基因表达式编程算法的麦蚜种群模型和建筑工程造价预测模型,建模效果优越,预测精度较高。
[Abstract]:Gene expression programming algorithm (Gene Expression Programming,GEP) is a new kind of intelligent evolutionary algorithm dealing with high dimensional and uncertain factors. It can mine hidden knowledge in data, such as rules, models, etc. And no prior knowledge is required. This algorithm has attracted the attention of many researchers at home and abroad with its unique coding method, excellent data mining ability and processing ability of highly nonlinear systems, and has been widely used in many practical fields. The main work of this paper is to improve the standard GEP algorithm and apply it to the two major problems of knowledge discovery, that is, wheat aphid population modeling and construction engineering cost prediction. It can provide basis for prediction of population occurrence of wheat aphid, feasibility study of construction project and reasonable design scheme. The specific work of this study is as follows: (1) on the basis of reading a lot of literature and modeling and forecasting content, this paper summarizes the key knowledge of the application field. This paper introduces the process and present situation of the population modeling of wheat aphid, and the extraction and classification of the characteristic quantity of construction engineering cost prediction. The principle and coding method of gene expression programming algorithm are summarized, and the flow and basic operation of the algorithm are introduced in detail. (2) according to the idea of human intervention, In this paper, a gene expression programming algorithm (DSCE-GEP) is proposed, which consists of artificial intervention system and natural evolution system. The artificial intervention system includes individual intervention and population intervention. Individual intervention is to improve the quality of individuals by using high quality gene pool to improve the quality of individuals. Population intervention is to use information entropy to improve population diversity by introducing random individuals and mirrored individuals. (3) aiming at the proposed improved algorithm, this paper makes a comparative simulation experiment with similar algorithms. The validity and advancement of DSCE-GEP algorithm are verified. At the same time, the DSCE-GEP algorithm is applied to agriculture and construction, to model the wheat aphid population data provided by the Chinese Academy of Agricultural Sciences, and to model and predict the construction project data listed in the literature. The experimental results show that the population model of wheat aphid based on genetic expression programming algorithm and the cost prediction model of construction engineering are superior to each other and the precision of prediction is high.
【学位授予单位】：西安建筑科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18
，
本文编号：2382194
[Abstract]:Gene expression programming algorithm (Gene Expression Programming,GEP) is a new kind of intelligent evolutionary algorithm dealing with high dimensional and uncertain factors. It can mine hidden knowledge in data, such as rules, models, etc. And no prior knowledge is required. This algorithm has attracted the attention of many researchers at home and abroad with its unique coding method, excellent data mining ability and processing ability of highly nonlinear systems, and has been widely used in many practical fields. The main work of this paper is to improve the standard GEP algorithm and apply it to the two major problems of knowledge discovery, that is, wheat aphid population modeling and construction engineering cost prediction. It can provide basis for prediction of population occurrence of wheat aphid, feasibility study of construction project and reasonable design scheme. The specific work of this study is as follows: (1) on the basis of reading a lot of literature and modeling and forecasting content, this paper summarizes the key knowledge of the application field. This paper introduces the process and present situation of the population modeling of wheat aphid, and the extraction and classification of the characteristic quantity of construction engineering cost prediction. The principle and coding method of gene expression programming algorithm are summarized, and the flow and basic operation of the algorithm are introduced in detail. (2) according to the idea of human intervention, In this paper, a gene expression programming algorithm (DSCE-GEP) is proposed, which consists of artificial intervention system and natural evolution system. The artificial intervention system includes individual intervention and population intervention. Individual intervention is to improve the quality of individuals by using high quality gene pool to improve the quality of individuals. Population intervention is to use information entropy to improve population diversity by introducing random individuals and mirrored individuals. (3) aiming at the proposed improved algorithm, this paper makes a comparative simulation experiment with similar algorithms. The validity and advancement of DSCE-GEP algorithm are verified. At the same time, the DSCE-GEP algorithm is applied to agriculture and construction, to model the wheat aphid population data provided by the Chinese Academy of Agricultural Sciences, and to model and predict the construction project data listed in the literature. The experimental results show that the population model of wheat aphid based on genetic expression programming algorithm and the cost prediction model of construction engineering are superior to each other and the precision of prediction is high.
【学位授予单位】：西安建筑科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18
，
本文编号：2382194
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