Afin de réduire le coût de calcul pour des problèmes d'optimisation coûteuse, cette thèse a été consacrée à la Stratégie d'Evolution avec Adaptation de Matrice de Covariance assistée par modèle de Krigeage (KA-CMA-ES). Plusieurs algorithmes de KA-CMA-ES ont été développés et étudiés. Une application de ces algorithmes KA-CMA-ES développés est réalisée par l'identification des paramètres matériels avec un modèle constitutif d'endommagement élastoplastique. Les résultats expérimentaux démontrent que les algorithmes KA-CMA-ES développés sont plus efficaces que le CMA-ES standard. Ils justifient autant que le KA-CMA-ES couplé avec ARP-EI est le plus performant par rapport aux autres algorithmes étudiés dans ce travail. Les résultats obtenus par l'algorithme ARP-EI dans l'identification des paramètres matériels montrent que le modèle d'endommagement élastoplastique utilisé est suffisant pour décrire le comportement d'endommage plastique et ductile. Ils prouvent également que la KA-CMA-ES proposée améliore l'efficace de la CMA-ES. Par conséquent, le KA-CMA-ES est plus puissant et efficace que CMA-ES pour des problèmes d'optimisation coûteuse. / In order to reduce the cost of solving expensive optimization problems, this thesis devoted to Kriging-Assisted Covariance Matrix Adaptation Evolution Strategy (KA-CMA-ES). Several algorithms of KA-CMA-ES were developed and a comprehensive investigation on KA-CMA-ES was performed. Then applications of the developed KA-CMA-ES algorithm were carried out in material parameter identification of an elastic-plastic damage constitutive model. The results of experimental studies demonstrated that the developed KA-CMA-ES algorithms generally are more efficient than the standard CMA-ES and that the KA-CMA-ES using ARP-EI has the best performance among all the investigated KA-CMA-ES algorithms in this work. The results of engineering applications of the algorithm ARP-EI in material parameter identification show that the presented elastic-plastic damage model is adequate to describe the plastic and ductile damage behavior and also prove that the proposed KA-CMA-ES algorithm apparently improve the efficiency of the standard CMA-ES. Therefore, the KA-CMA-ES is more powerful and efficient than CMA-ES for expensive optimization problems.

過去的文獻中，資產配置的方法不外乎效率前緣、動態資產配置等方式，但是，單獨針對銀行探討的文章並不多見，所以本文的貢獻在於單獨針對銀行的資產配置行為進行研究，希望能利用『演化策略演算法』，進行『模擬最佳化』來解決銀行資產配置的問題。基本上這個方法是由兩個動作結合而成，先是模擬，再來尋求最佳解。所以，資產面我們選擇了現金、債券、股票、不動產四項標的，而負債面則模擬了定存、活存與借入款這三項業務，然後透過重複執行模型的方式來求出最適解。並與單期資產配置方法下的結果作一比較，發現運用演化策略演算法有較佳的結果，此外，在不同的亂數下，仍具有良好的穩健性，可作為一般銀行經理人參考之用。 / We focus on the bank’s asset allocation problem in this thesis. We use simulation optimization to solve the problem by evolution strategy, which is relatively new in the financial field. Simulation optimization consists of two steps: simulate numerous situations and search for the optimal asset portfolios. In the simulation, we set up four assets, including cash, bond, stock, and real estate and three business lines, including demand deposits, time deposits, and borrowings. Then we search for the optimal solution by running the ES algorithm. The results show that simulation optimization generates better results than one-period asset allocation. Furthermore, the evolution strategy method generates similar results using different random numbers.

Le sujet de cette thèse se rapporte à la génération d’impulsions ultracourtes dans une cavité laser fibrée à travers l’optimisation automatique de ses paramètres par un algorithme d’évolution. L’intérêt pour cette problématique provient de la difficulté à explorer les dynamiques impulsionnelles de manière systématique dans un large domaine de paramètres expérimentaux. Nous avons montré que l’implémentation d’un algorithme d’évolution sur une cavité laser fibrée de ce type peut être réalisée, en prenant les précautions adéquates pour que cette association soit la plus efficace possible. Nous avons démontré expérimentalement pour la première fois le verrouillage de modes depuis la seule optimisation des contrôleurs de polarisation utilisant une procédure automatique d’auto-apprentissage. Nous avons démontré que la sélection du blocage de modes depuis son spectre radio-fréquence permet de sélectionner le taux de répétition desimpulsions à l’intérieur de la cavité. Ces résultats préliminaires démontrent les potentialités de notre méthode employée dans des situations de dynamique non linéaire ultrarapide de grande complexité, particulièrement sensibles aux paramètres. / This thesis deals with the generation of ultrashort pulses within a fiber laser cavity through the automatic optimization of its parameters by an evolutionary algorithm. The interest of this subject comes from the difficulty to systematically explore dynamics in a large domain of experimental parameters. We have shown that it is possible to implement an evolutionary algorithm on fiber laser cavity with appropriate precautions. We have experimentally demonstrated for the first time the mode locking of a laser cavity only using the optimization of polarization controllers through an automatic and self-learning procedure. We also have demonstrated that selecting the mode locking from it radio-frequency spectrum allow to select the pulses repetition rate within the cavity. These preliminary results show the promising aspect of our method used in situations of non linear ultrafast dynamics with high complexity which are particularly sensitive to parameters.

This thesis summarizes knowledge in the field of time series theory, method for time series analysis and applications in financial modeling. It also resumes the area of evolutionary algorithms, their classification and applications. The core of this work combines these knowledges in order to build a system utilizing evolutionary algorithms for financial time series forecasting models optimization. Various software engineering techniques were used during the implementation phase (ACI - autonomous continual integration, autonomous quality control etc.) to ensure easy maintainability and extendibility of project by more developers.

協同規劃、預測與再補貨(Collaborative Planning, Forecasting and Replenishment; CPFR)，是目前供應鏈管理下重要的討論議題；台灣近年來由於加入WTO與製造業外移使競爭壓力加劇，全球運籌需求提升，使廠商間的合作更加密切，且近年來企業資訊環境與基礎建設逐漸成熟，有助於協同商務之發展。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同且績效良好的銷售預測具有關鍵的重要性，是管理決策與協同合作時的重要依據；但是多數的企業並沒有一個結構化、有系統化的預測流程及方法，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。 在CPFR流程下，強調買賣雙方透過完整、即時資訊的交流，進行短期、單一銷售預測，以提供雙方後續訂單預測、訂單補貨等決策的依據。本研究利用演算法（類神經網路和演化策略法）找出更適合混合性預測架構的解釋變數，再以較適合於實數解之演化策略法於修改黃蘭禎（2004）的三階段之預測模型架構，最後採用實驗方法，進行模型績效驗證。 / Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. Because of the severer competition resulted from entrance into WTO and industry integration, cooperation between Taiwanese companies becomes more intensely; enterprises’ information environment and foundation construction attain to maturity also boost the development of collaboration business. In CPRF process and supply chain operation environment, it is critical that a good performance sale forecasting collaborated by both supplier and buyer sides, and it is also the basis of policy decision and collaboration. However, the majority of the companies lack for a structural and systematical forecasting process to proceed with a multi-points forecasting with different methods. This kind of sale forecasting is less of stable quality and is harder to provide the managers a reasonable statistics explanation. Under the CPRF process, both buyers and sellers are able to obtain the short-term and single sale forecasting by real time information communication. Furthermore, the follow-up order forecasting and replenishment strategy decision can be also established through this process. This research finds the variables that are more suitable to the mixed structure by usage of the algorithms, ANN and Evolution Strategy. And this research uses Evolution Strategy that is more suitable to real question to improve the mixed structure of Huang (2004). In the end, experimentation is adopted in order to verify the performance of the model.

The aim of this work was to create a system for optimisaton of specific analog circuits by evolution using multiple fitness functions . A set of experiments was run, and the results analyzed to evaluate the feasibility of evolutionary optimisation of analog circuits . A requirement for this goal is the study and choice of certain types of analog circuits and evolutionary algorithms . For the scope of this work , amplifiers and oscillators were chosen as target circuits , and genetic algorithms and evolutionary strategies as evolutionary algorithms . The motivation for this work is the ongoing effort to automate the design and optimisation of analog circuits , where evolutionary optimisation is one of the options .

The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.