| 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 |
|---|---|---|---|---|
| 6105085 | 演化式計算 | Evolutionary Computing | 3.0 | 3 |
| 中文概述 Chinese Description | 演化計算最佳化與傳統最佳化的最主要差別在於演化式計算係透過迭代方式逼近最佳解。由於所使用的目標函數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，因此更適合處理實務的工程問題。演化式計算緣起於演化式規劃，基因演算法則透過數學模型的推導，驗證其可以使用在解決最佳化問題。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 Evolutionary Computing and conventional computing in that it obtains the near optimal solution through iterations. Because of the characteristics of the fitness functions, the evolutionary computation is more tolerable for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. As a result, the evolutionary computation can be more suitable for solving engineering problems. The origin of the evolutionary computation is from the Evolutionary Planning. Through a formal mathematic modeling, the Genetic Algorithm is proved to be used to solve optimization problems. 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), Simulated Anneal (SA) will also be discussed. | |||
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