本篇論文研究演化的動態過程中的隨機穩定性。演化過程中,突變(mutation)或變異隨時可能會發生。因此,演化中不存在安定(steady)或是穩定(stable)的狀態。但是當突變機率趨近於零時,有些狀態在長期間比其他狀態容易出現在過程中為人所觀察到。這些狀態稱為隨機穩定狀態(stochastically stable state)。我們發展出一具有一般性的演算法來找出所有的隨機穩定狀態。有別於傳統演算法,這套演算法大幅降低計算所需次數。透過這套演算法,我們定義了一個集合: stable set。我們發現,stable set包涵了所有的隨機穩定狀態。同時,我們也提出數個隨機穩定狀態的充份條件。這些發現代表著,分析演化模型的假設及均衡(equilibria)性質之間的關係是可行的。 / We study the behaviors of the evolutionary models with persistant noises through a general algorithm which describes the relationships among the stochastic potentials. That is, by constructing a closed loop on the graph of the directed trees, we show that the comparison among the stochastic potential is equivalent to the comparison among one-step transition costs. Hence, we are able to systematically analyze the properties of the stochastically stable states. Our main nding is that the set of the stochastically stable states is contained in a set, which we dene as a stable set. Each state in this set is difcult to escape from and is resistant to the attraction of any other states in the stable set. Based on this nding, related sufficient conditions for the stochastically stable states are presented, and some results in the literature are also reinterpreted. In addition, we show that this algorithm drastically reduces the necessary steps for characterizing the stochastically stable states. This means that the analysis on relationships between the assumptions of the model and the properties of equilibria are possible and promising.

科技進步使人類生活不斷改善，產業的發展逐漸轉變，服務業的崛起已成為世界趨勢。在資訊科技的推動下，服務業除了關注人與人的互動與商業的交換之外，科技漸漸成為另一個重要因素，服務產業的核心轉為由科技、人、及商業流程所組成，新的科學理論─「服務科學」應運而生。服務科學的目的在整合各領域之知識以促進服務創新。另一方面，服務產業的重要成員─數位內容產業正迅速發展，在數位內容創作領域中，消費者與生產者角色逐漸模糊，成為協同互利的夥伴，這樣的轉變衍生許多新的問題有待解決，包括夥伴關係如何建立與平台機制的發展等。因此，透過服務科學解決數位內容產業的問題，當是一項值得採用之方法。本研究透過服務科學的三個面向─服務組成、服務流程、以及服務價值作為研究的背景架構，並採用結合語意網路、模糊規則、基因演算法所組成之語意式模糊基因演算法作為數位內容問題的解決方案，在電子市集的環境推動下，以協同式夥伴配對的方式，達到使用者的創作利益。系統共分三大服務組成：本體發展模組、語意式模糊基因演算夥伴配對模組、以及協同價值評價模組，以語意定義創作問題與產品的概念，並透過基因演算法改善模糊規則，釐清概念間的關係，最後透過市場機制完成配對達成雙方利益。本系統之預期貢獻分為：(1)利用服務科學改善數位內容問題。(2)為服務科學方法之應用提供發展方向。 / The economies of the world have been shifting labor from agriculture and manufacturing into services. In the emergent concept of service science, competition will center on value co-creation experiences with information technology and service innovation refers to invented service system designs yielding values to real service problems. This paper presents a novel service system design for the digital content industry. This service design is unfolded with a marketplace featuring producers/consumers collaboratively co-creating digital contents and a self-regulating mechanism enabled by a semantic-based fuzzy genetic approach. In the marketplace, the roles of consumers and producers blur, and they are partners who collaborate to attain mutual benefits. The service system encompasses three service components (ontology developer, S-FGA partnership matcher and co-created value appraiser) that altogether work to empower producers/consumers who can effectively co-create their digital contents in a novel collaborative way. In addition to presenting a solution to digital content creation, this paper also showcases a new methodology for service innovation referred in service science.

A*(A-Star)演算法透過啟發函式，以減少路徑搜尋過程中所需要計算的網路數量，SMA*(Simplified Memory Bounded A-Star)為A*之變形，目前最廣泛被應用於GPS導航系統之路徑規劃的演算法。尋找路徑的計算過程中，A*與SMA*演算法利用中間節點與目的地的方向(直線距離)作為啟發函式，以預估中間節點到目的地之路徑長度挑選優先搜尋的路段，而SMA*則因記憶體的限制會排除預估長度較長的路段，以減少搜尋的路段數量與記憶體之使用量。當起點與終點中存在障礙地形時或路段較崎嶇時，以方向導引路徑搜尋之準確度便大幅降低，導致A*與SMA*之搜尋數量增加，SMA*甚至會得到較差的路徑。 主幹導引式最短路徑搜尋演算法(Backbone Orientation)以骨幹路徑導引路徑之搜尋，在障礙地形或道路崎嶇的情況下，可有效避免SMA*之缺點，效能較佳。主幹導引式最短路徑搜尋演算法分為二階段，先由原始路網中提取骨幹路網，並計算出最佳骨幹路徑，再利用骨幹路徑引導路徑的搜尋，在骨幹路徑的一定範圍內搜尋最短路徑。 本研究以台灣地區2007年之平面路網圖進行實驗，以三種不同的實驗方式進行實驗，以驗證主幹導引式最短路徑搜尋演算法之效能，證明在SMA*演算法之啟發函式效能低落時，使用主幹導引式最短路徑搜尋演算法可以有效的改善SMA*在障礙地形之效能不彰的問題。

安全多方計算是資訊安全研究裡的一個重要主題，其概念為多方在不洩漏各自私有資訊下能一起完成某種函式的計算。在安全多方計算研究領域裡，有一種作法是以scalar product來當作計算的基礎演算邏輯單元，重而建構其他更複雜的安全多方計算。本論文首先針對scalar product發展一套平行性實作架構，藉此我們再實作出多個不同演算法之comparison計算，其中包含了循序演算法以及平行演算法。我們透過實驗來找出適當的平行計算基礎架構與影響執行時間效能的主要因子，並以執行時間效能上的分析來推導相關時間公式。由上述實證研究我們對於不同演算法之comparison計算來作執行時間效能的預測，從實驗結果可以得知我們推導出來之時間公式極為準確，希望能給予使用者在執行comparison計算有所考量，使其在不同執行環境執行comparison計算能有最佳的執行時間效能。 / Loosely speaking, secure multi-party computation (SMC) involves computing functions with inputs from two or more parties in a distributed network while ensuring that no additional information, other than what can be inferred from each participant’s input and output, is revealed to parties not privy to that information. This thesis concerns the parallel implementation of SMC using a scalar-product (SP) based approach. In this approach, SP is considered as the basic building block for constructing more complex SMC. My thesis first develops a concurrent architecture for implementing two-party scalar product computation. Then it implements several algorithms of secure comparison. Finally, a series of experiments are conducted to collect performance statistics for building time functions that can predict the execution time of comparison computation based on that of the scalar product and other parameters, such as CPU core numbers. From the experimental results, we find that these time functions are very accurate. Hence we argue that these time functions can assist users to obtain the better runtime performance for comparison protocols under their specific execution environments.

程序式動畫是一種根據使用者所提供的高階運動參數，自動產生動畫的方法。藉著高階的運動參數，程序式動畫比運動擷取資料有著更高的彈性。使用者可透過調整參數，輕易地讓動畫滿足情境上所需的限制。但如何調整適當的運動參數以產生擬真的動畫仍屬不易，因此程序式動畫常有在視覺上觀感不自然的問題。本研究的目標是，將運動擷取資料擬真的要素，帶到程序式動畫之中，以改進程序式動畫的品質。我們將問題定義成一個最佳化問題：給定一段運動擷取資料，系統該如何調整程序式動畫之參數，使得程序式動畫與運動擷取資料的差距盡可能地縮小？我們的系統可以參考一段運動擷取資料，以最佳化演算法，自動調整程序式動畫的參數，搜尋能產生出與運動擷取資料最為相似的運動參數。為了進一步讓產生之動畫符合環境的限制需求，多組最佳化過後的運動參數可以再透過內插，重新產生出一組符合限制需求的運動參數。實驗結果顯示，我們的方法不但使程序式動畫得以保留原來彈性的優點，也改善了程序式動畫常有的視覺觀感不自然的缺點。 / Procedural animation provides a way for a user to generate animation according to the high-level motion parameters that the user supplies. With the high-level motion parameters, procedural animation has the flexibility of generating animation accommodating the requested constraints in a scenario. However, tuning parameters to generate realistic animations usually is a difficult task. Therefore, animations produced with this approach often have the drawback of unrealistic-looking. Our goal is to improve the quality of procedural animation by bringing the naturalness of motion capture data into procedural animation. We model our problem as an optimization problem: given a motion captured clip, how does the system tune the motion parameters in an animation procedure to minimize the difference between animations produced by a procedure and captured in a motion clip? Our proposed system takes a motion captured clip as a reference and tunes the motion parameters of the animation procedure with an optimization algorithm. In order to generate animation satisfying environmental constraints, multiple optimized motion parameters can be interpolated to create a new set of motion parameters which can also satisfy the constraints. Our experimental results show that our method not only retains the flexibility of procedural animation, but also enhances the quality of procedural animation.

隨著服務體驗經濟時代的來臨，服務提供商所面臨到的環境是愈來愈競爭及逐漸是轉由消費者所主導的型式，因此服務提供商如何去滿足顧客的需求並且達成更高的滿意度便是主要的目標之一。特別是在發展型服務提供者的環境下，他們需要去考量自已本身較不充足的服務能力及資源來設計及提供服務給顧客，而這樣的條件之下，他們也很難在短時間及時的去改善並且提供穩定的服務品質。因此，本研究提出一套是基於顧客期望理論及粒子群演算法的架構下所發展的協同式互動設計機制，希望協助發展型服務提供商解決他們所面臨的問題及創造出更大的服務價值。 本研究將協同式互動設計機制應用在會展產業的服務環境底下，並利用模擬實驗的方式去驗證此機制的有效性及鞏固性。協同式互動設計機制共有四大模組：(1)顧客偏好識別模組 (2) 粒子群期望因子選擇模組 (3) 情境式旅程抉擇模組 及(4) 服務執行模組。本研究設計此機制時考量了加入顧客間互動的能力來幫助發展型服務提供商進行更有效的服務互動並且執有效的顧客群體期望控制的目標，以便在減輕服務提供商所付出的成本之下，還能達成良好的顧客滿意度。而本研究的研究貢獻為幫助發展型服務提供商解決他們所面臨的挑戰，並且在有限的資源和能力底下，仍然可以使得他們保持與高能力服務廠商之間的競爭;而另一貢獻為在整體的服務環境底下，能讓所有的參與角色都能夠得到最大的價值，而形成一個高效能的服務生態系統。 / With the progressive advancement of the technology and fiercely-competitive environment in recent years, customers have paid more attention to the issue that how diversity and rich the service experience could satisfy their needs; in other words, the service providers must acquire the competitive advantage among other service competitors by pondering on that how to deliver the qualified service offerings in every service encounter to achieve the objective of customer satisfaction. On the other hand, many research findings noted that customers’ service quality evaluation in service encounter were influenced by the comparison between the customer expectation toward service and the service performance that they perceived; therefore, managing the customer expectation becomes the vital part concerning the customer satisfaction. Furthermore, the shortcomings of the low-moderate competence service providers is that they could not provide the constant qualified service offerings to customers in each service interaction in terms of the reason for lesser service capability and resource. Consequently, this study propose the collaborative interaction design approach which based on the Particle Swarm Optimization(PSO) algorithm to generate the dynamical service interaction among the service providers and customers for the low-moderate competence service providers and aids them to control their group customers’ expectation by collaborating with customers; in other words, the service effort of the service provider could be lightened by engaging the customer capability and the service offerings could be enhanced to provider for customers. Therefore, this study utilizes the four modules in the research framework to achieve the aforementioned objective. Ultimately, the expected contributions of this study are two-folds: (1) Aid the low-moderate competence service providers to improve the service experience for customers on the restriction of lesser service capability. (2) Utilize the PSO algorithm to decide the determinants that effectively influence on customers’ expectation considering the whole benefits among stakeholders. Hence, the collaborative interaction design proposed in this study has conspicuous benefits for the low-moderate competence service providers to preserve the competitive advantage by providing the well-design exemplar to let them follow.

在科技持續進步的時代，金融市場發展隨著社會的演進不斷地成長與活絡，金融商品也從原本單純的本國存放款、外幣投資衍生出票券、債券等利率投資工具；除此之外，隨著資本市場的擴張，股票、基金、期貨與選擇權等投資標的更是琳瑯滿目。 而後產生了許多人使用資料探勘工具預測市場的買賣時機。如Baba N., Asakawa H. and Sato K.(1999)使用倒傳遞類神經網路來預測到股市未來的漲跌，而後又在2000年研究當中加入基因演算法來求得倒傳遞類神經網路的權重，得到最後的類神經網路模型。 在做資料探勘的同時，我們得在希望預測目標(Target)上事先定義好一套固定規則，這會使得模型的彈性與可預測度降低，本研究希望能透過資料探勘工具增加預測目標規則的彈性，增加模型最後的預測準確度。 本研究樣本區間選用2010年到2015年的台指期貨數據做為資料，並結合羅吉斯回歸與粒子群演算法建構更加有彈性的預測模型結果，最後發現在未來10分鐘，若漲幅超過0.1114%做為買進訊號的話，其建立出的模型可達到84%的預測準確度。

我們將原本求只有實根的多項式問題轉換為利用QR方法求一個友矩陣(companion matrix)或是對稱三對角(symmetric tridiagonal matrix)的特徵值問題，在數值測試中顯示出利用傳統演算法去求多項式的根會比求轉換過後矩陣特徵值的方法較沒效率。 / Given a polynomial pn(x) of degree n with real roots, we transform the problem of finding all roots of pn (x) into a problem of finding the eigenvalues of a companion matrix or of a symmetric tridiagonal matrix, which can be done with the QR algorithm. Numerical testing shows that finding the roots of a polynomial by standard algorithms is less efficient than by computing the eigenvalues of a related matrix.

每股盈餘是公司的重要財務資訊之一，它可以反應公司的經營績效，因此一方面可以提供給投資者作為投資決策之參考，另一方面提供給管理者作為管理評量的參考指標之一。過去在每股盈餘等財務預測往往以統計方法進行，因此在自變數選擇上常受到限制，同時有些預測模式其輸出結果往往只能以常長或衰退等二元式的結果表示。而另一方面，以類神經網路預測方式的預測模式可能因變數增加，使得網路變的較複雜。本研究嘗試以人工智慧中的遺傳演算法來作為預測的工具，發展財務預測模型，來預測每股盈餘，解決過去預測方式的限制或缺點。同時也將對過去的遺傳演算法稍做修正，並嘗試以實際值的編碼方式進行編碼，以符合需求。最後進一步比較遺傳演算法和其他預測方式，瞭解以遺傳演算法做於預測每股盈餘工具的特性及優缺點。 / Earnings per share (EPS) is one of the important financial indicators to a corporation. It reflects the operating performance of a corporation. On one hand, EPS provides information available to investors for decision making; on the other hand, it is an indexfor measurement of management. In the past, financial forecasting was often done by using statistical models. However, the input variables were limited by using these statistical models. Besides, some stastical models only provide dichotomy output ,such as either "grwoth" or"decline". The neural network forecasting model will be more of complexity, when the input variable increases. This research attempts to develop a financial forecasting model to forecast the EPS by using the Genetic Algorithms, which is a new topic of artificial intelligence. This model excludes both the limitations and disadvantages of the models mentioned above. Here, the genetic algorithms will be modified and the real number will be used to code as a gene of achromosome to meet the requirements of the finacial model. Finally,we compare the genetic algorithms financial forecasting model with the other ones in order to understand the features, advantages and disadvantages of genetic algorithms as being a financial forecasting tool .

時間序列分析發展至今,常常發現動態資料的走勢,隨著時間過程而演變.所以傳統的模式配適常無法得到很好的解釋,因此許多學者提出不同的模型建構方法.但是對於初始模式族的選擇,卻充滿相當的主觀與經驗認定成份.本文針對時變型時間序列分析,考慮利用知識庫,由模式庫來判斷初始模式.再藉由遺傳演算法的觀念,建立模式參數的遺傳關係.我們把這種遺傳演算法,稱之為時變遺傳演算法.針對台灣省國中數學教師人數,分別以時變遺傳演算法,狀態空間,與單變量ARIMA來建構模式,並作比較.比較結果發現,時變遺傳演算法較能掌握資料反轉的趨勢,且預測值增加較為平緩.因此時變遺傳演算法在模式建構上將是個不錯的選擇. / In time series analysis, we find often the trend of dynamic data changingwith time. Using the traditional model fitting can't get a good explanationfor dynamic data. Therefore, many savants developed a lot of methods formodel construction. However, these methods are usually influenced by personal viewpoint and experience in model base selection. In this thesis, we discussedtime-variant time series analysis. First, we builded a model base to judge inial models by knowledge base. Then, we set up the genetic relations of themodels' parameter. This method is called Time Variant Genetic Algorithm. We use the data if the number of junior high school mathematic teachers in Taiwan to ccompare the predictive performance of Time Variant Genetic Algorithmwith State Space and ARIMA. The forecasting performance shows the Time VariantGenetic Algorithm takes a better prediction result.