本文選題：基因表達(dá)式編程　切入點(diǎn)：粒子群優(yōu)化　出處：《浙江工業(yè)大學(xué)》2014年碩士論文

【摘要】：數(shù)據(jù)挖掘中的分類是當(dāng)今計(jì)算機(jī)應(yīng)用技術(shù)和理論研究中的熱門領(lǐng)域,它作為一種有效的數(shù)據(jù)分析手段,具有廣泛的應(yīng)用。將進(jìn)化計(jì)算與分類技術(shù)結(jié)合,形成基于進(jìn)化計(jì)算的分類方法是其中一個(gè)重要的研究方向�；虮磉_(dá)式編程(Gene Expression Programming, GEP)和粒子群優(yōu)化(Particle Swarm Optimization, PSO)算法是兩種新型進(jìn)化計(jì)算方法,它們分別通過(guò)模擬生物進(jìn)化機(jī)制和鳥類覓食行為搜索問(wèn)題的最優(yōu)解。本文以GEP和PSO為工具,研究這兩種進(jìn)化計(jì)算方法在基于距離的分類方法中的應(yīng)用。本文的主要工作和成果如下： 1.介紹了基因表達(dá)式編程和粒子群優(yōu)化算法,包括算法的起源、基本流程和相關(guān)概念。在此基礎(chǔ)上,比較了這兩種進(jìn)化計(jì)算方法的異同點(diǎn)。 2.詳細(xì)闡述了基于距離的分類方法的基本原理。提出了基于GEP的類中心點(diǎn)分類算法,引入了一種新的運(yùn)算符,研究了該算法中GEP個(gè)體的編碼和解碼問(wèn)題。多個(gè)數(shù)據(jù)集上的實(shí)驗(yàn)證明基于GEP的類中心點(diǎn)分類算法具有較強(qiáng)的搜索能力,且分類效果較好。針對(duì)基于GEP的類中心點(diǎn)分類算法后期存在收斂速度變慢,易陷入局部最優(yōu)值的情況,引入了PSO算法,提出了基于PSO的GEP分類算法,解決了該算法從GEP階段轉(zhuǎn)換到PSO階段過(guò)程中兩類種群個(gè)體的編碼兼容問(wèn)題。實(shí)驗(yàn)證明基于PSO的GEP分類算法陷入局部最優(yōu)值的情況減少,且分類精度較高。 3.推廣算法的應(yīng)用領(lǐng)域,將基于PSO的GEP分類算法應(yīng)用于面向?qū)ο蟮倪b感圖像分類,提出了基于GEPSO (GEP and PSO)模型的面向?qū)ο筮b感圖像分類方法。在加拿大安大略省倫敦市中部航空正射影像上的實(shí)驗(yàn)證明,基于GEPSO模型的面向?qū)ο筮b感分類方法具有可行性
[Abstract]:Classification in data mining is a hot field in computer application technology and theoretical research. As an effective means of data analysis, it has a wide range of applications.It is an important research direction to combine evolutionary computing with classification technology to form a classification method based on evolutionary computing.Gene Expression programming (GP) and particle swarm optimization (PSO) are two new evolutionary algorithms, which simulate the evolutionary mechanism of organisms and the optimal solution of bird foraging behavior.In this paper, we use GEP and PSO as tools to study the application of these two evolutionary computing methods in distance-based classification.The main work and results of this paper are as follows:1.This paper introduces the genetic expression programming and particle swarm optimization algorithm, including the origin, basic flow and related concepts of the algorithm.On this basis, the similarities and differences between the two evolutionary computing methods are compared.2.The basic principle of distance-based classification method is described in detail.A class center point classification algorithm based on GEP is proposed, and a new operator is introduced. The coding and decoding problems of GEP individuals in this algorithm are studied.Experiments on multiple data sets show that the algorithm based on GEP has strong searching ability and good classification effect.In view of the situation that the convergence rate of the class center point classification algorithm based on GEP becomes slow and easy to fall into the local optimal value in the later stage, the PSO algorithm is introduced, and the GEP classification algorithm based on PSO is proposed.This algorithm solves the problem of coding compatibility between two classes of individuals in the transition from GEP stage to PSO stage.The experimental results show that the GEP classification algorithm based on PSO has less local optimal value and higher classification accuracy.3.In this paper, the GEP classification algorithm based on PSO is applied to object oriented remote sensing image classification, and an object oriented remote sensing image classification method based on GEPSO GEPSO and PSO model is proposed.The experiment on the aerial orthophoto image of the central city of London, Ontario, Canada proves that the object-oriented remote sensing classification method based on GEPSO model is feasible.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13;TP751

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