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研究生:王鼎元
研究生(外文):Ding-Yuan Wang
論文名稱:調適性演化基因體遺傳演算法之設計與評估
論文名稱(外文):Design and Evaluate Adaptive Evolutionary Genomic Genetic Algorithm
指導教授:林文修林文修引用關係
指導教授(外文):Wen-Shiu Lin
學位類別:碩士
校院名稱:輔仁大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:118
中文關鍵詞:演化式計算遺傳演算法機會發現演化基因體基因工程
外文關鍵詞:Evolutionary ComputationGenetic Algorithm (GA)Chance DiscoveryEvolutionary GenomicGenetic Engineering
相關次數:
  • 被引用被引用:2
  • 點閱點閱:234
  • 評分評分:
  • 下載下載:44
  • 收藏至我的研究室書目清單書目收藏:0
近年來演化式計算不斷的蓬勃發展,並成功地應用在許許多多的領域上,其中的遺傳演算法 (Genetic Algorithms, GAs) 對於處理線性以及非線性問題都有著不錯的效果。然而遺傳演算法卻存在著一些缺陷,例如說在某些問題下,往往只會搜尋到區域的最適解,而無法跳脫出來找到全域的最適解,造成演化上不利的現象。本研究利用基因體的概念,強化探索力,並應用機會發現的概念,輔以基因工程配合傳遺傳演算法,企圖改善傳統遺傳演算法,提升其演算法的效率以及效果。
因此,本研究以台灣五十成分股與台灣一百中型股為對象,探討一些不同參數與設定而造成的演化現象,並且試著進行買進持有的實際投資,觀察報酬走勢,再比較傳統遺傳演算法,與運用演化基因體計算模組的調適性演化基因體遺傳演算法;實驗結果顯示出實際投資走勢上,以台灣五十成分股為解答空間,需買進持有五個月才有正向報酬,台灣一百中型股於買進持有三個月後開始有正向報酬;從演算法角度來觀察,演化基因體有不錯的探索能力,從廣大的解答空間搜尋優秀的基因體 (基因片段) 加以保存起來,作為跳脫區域解答的機會;若配合基因工程與遺傳演算法互動,則能擅用此一機會,跳脫區域解,往全域最適解的方向前進,調適性演化基因體就是利用演化基因體的強化探索力,再結合遺傳演算法穩健的利用力搜尋,交互結合之下,其成效將較傳統的遺傳演算法高出許多。
In recent years the Evolutionary Computation continually flourish, and successfully applied in many domains, The Genetic Algorithms for dealing with linear and nonlinear problems is a good effect. However, the genetic algorithm has some shortcomings exist. For example, when genetic algorithm deals with some specific problem, it can only search for the optimal solution in the local region and the inability to find the global optimal solution, cause adverse evolution of the phenomenon. Our research use the concept of genomic to enhance exploration, and use the concept of chance discovery, supplemented by genetic engineering with simple genetic algorithms, Try to improve simple genetic algorithm, to enhance its efficiency and effectiveness.
Hence, our research's experimental subjects are the member of TSEC Taiwan 50 index and TSEC Taiwan 100 index, discuss the different parameter sets and settings caused by the phenomenon of evolution. And then, we try to buy and holders of the portfolio, observation remuneration trends and then compare the simple GA, the evolutionary genomics and the adaptive evolutionary genomic. Experimental results show that the trend on the actual investment, the alternative space is the member of TSEC Taiwan 50 index, its need to be brought and held until five months for pay. Besides, the member of TSEC Taiwan 100 index need to be brought for held until three months for pay. From the perspective of algorithm, evolutionary genomic has excellent ability to explore, and it can find the great genomics (the gene fragments) to be preserved, and then escape from the region as the opportunity to solutions. When genetic engineering interacts with genetic algorithms, they can use this opportunity to escape form the local region to find the optimal solution in the direction of the global region. The adaptive evolutionary genomic use the enhance exploration of evolutionary genomic and combine the exploitation by genetic algorithm, the effectiveness is better than simple genetic algorithm.
表  次 vi
圖  次 ix
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第貳章 文獻探討 5
第一節 遺傳演算法 5
第二節 基因工程演算法 14
第三節 機會發現 15
第四節 Schemata、Schema theorem 以及Building Blocks 16
第五節 效率前緣 18
第六節 函數與指標 19
第七節 本章小結 21
第參章 研究設計 23
第一節 系統架構 24
第二節 演化基因體演算法架構設計 27
第三節 實驗設計 35
第四節 系統效能測試 38
第肆章 實驗結果與分析 41
第一節 實驗參數 41
第二節 演化實驗結果 (台灣五十成分股) 42
第三節 演化實驗結果 (台灣一百中型股) 51
第四節 族群大小100與族群大小200 77
第五節 演化趨勢與實際投資走勢 89
第伍章 評估與討論 101
第一節 實驗結果與討論 101
第二節 評估與分析 106
第陸章 結論與建議 113
第一節 研究結論 113
第二節 研究貢獻 114
第三節 研究限制 115
第四節 未來研究與建議 115
參考文獻 116
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2.高崇晏,機會發現與基因工程技術在遺傳演算法演化績效改良之研究,輔仁大學資訊管理所碩士論文,2006。
3.謝雅涵,投資決策風格為基的最適投資組合之研究-交談式遺傳演算法之應用,輔仁大學資訊管理所碩士論文,2006。
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