| 課程編碼 Course Code | 中文課程名稱 Course Name (Chinese) | 英文課程名稱 Course Name (English) | 總學分數 Credits | 總時數 Hours |
|---|---|---|---|---|
| 6105054 | 軟性計算 | Soft Computing | 3.0 | 3 |
| 中文概述 Chinese Description | 軟性計算最佳化與傳統計算(硬性計算)最佳化的最主要差別在於軟性計算可以容忍對問題的不確定性，不精確性，部分正確性以及類似性。實際上，軟性計算的主要模型係依據人類思維的模式或是生物系統的運作而發展出來的。軟性計算的主要導引基礎為:利用不確定性，不精確性，部分正確性以及類似性，以獲得可追蹤，強健以及快速的解。軟性計算主要源於1965年由Zadeh所提出的模糊集理論，1973年提出的複雜系統分析與決策支援，1979年提出的軟性資料分析與機率理論。在更後期也陸續提出類神經計算與基因演算。本課程主要針對基因演算法(GA)與演進歸化法(EP/ES)進行討論，其他的方法如粒子群集(PSO)，模擬退火法(SA)也在本課程介紹之列。 | |||
| 英文概述 English Description | The differences between Soft computing and conventional (hard) computing in that it can tolerant imprecision, uncertainty, partial truth, and approximation. The key model for soft computing is the human mind or biological systems. The guiding principle of soft computing includes: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them Zadeh's 1965 paper on fuzzy sets; the 1973 paper on the analysis of complex systems and decision processes; and the 1979 report (1981 paper) on possibility theory and soft data analysis. The main focus of this course will be in Genetic Algorithm (GA) and Evolutionary Programming/Evolutionary Strategy (EP/ES). Other methods such as Particle Swarm Optimization (PSO) or Simulated Annealing (SA) will also be discussed. | |||
備註：