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 .