教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
108-2	274002	計算智慧	1	3.0	3	★	陳俊豪	資財四甲	2	0	陳俊豪老師 / 四甲、碩班合開 / 科研243

教學大綱與進度：

教師姓名	陳俊豪
Email	chchen6814@gmail.com
最後更新時間	2020-02-07 02:01:50
課程大綱	本課程將介紹計算智慧的相關知識、技術與其優點，旨在教授學生如何運用計算智慧技術解決實際問題。課程目標有二：(1)相關的計算智慧技術與其運作原理，包含：遺傳演算法、多目標遺傳演算法等；(2)相關的應用案例討論。透過此課程，學生將可套用計算智慧技術於各種最佳化問題。 This course will introduce related knowledge, techniques and merits of computational intelligence for providing useful ways for students to solve the real application problems. The goals of this course include two parts: (1) related computational intelligence techniques and how it work, including genetic algorithms, multi-objective genetic algorithm, etc; (2) related applications and discussions. Through this course, students can learn how to apply computational intelligence on different optimization problems.
課程進度	Week 1: Computational Intelligence Overview Week 2: GAs: What are they? (Simple Genetic Algorithms) Week 3: GAs: Why do they work? (Schema Theorem) Week 4: Binary or Float? (Real Code Genetic Algorithms) Week 5: Grouping Problem and Grouping Genetic Algorithm Week 6: Multi-objective Problems and Solutions (Multi-objective Genetic Algorithms) Week 7: Advanced Optimization techniques - SPEA, SPEAII Week 8: Advanced Optimization techniques - MOEA/D Week 9: Midterm Examination Week 10: Applications on Business Intelligence Week 11: Applications on Portfolio Management Week 12: Applications on Pattern Discovery Week 13: Case Discussion & Paper Study Week 14: Case Discussion & Paper Study Week 15: Case Discussion & Paper Study Week 16: Case Discussion & Paper Study Week 17: Final Project Presentation Week 18: Final Examination
評量方式與標準	出席：20％ 平時表現：20％ 作業與期中報告：20% 論文報告：20% 期末專題報告: 20%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：否 Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs (3ed) Data Mining : Concepts and Techniques, 3/e (Hardcover), Jiawei Han, Micheline Kamber, Jian Pei, 2011.
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