CS 5612-0

Brief

Evolutionary computation is a significant subfield of AI and serves as a powerful problem solver on search, optimization, and machine learning problems. This course provides an introduction to the algorithms, theory, and application of evolutionary computation geared towards the students who are interested in development and/or application of the subject. Topics in algorithms and theory include genetic algorithms, evolution strategies, genetic programming, parameter setting and analysis. Topics in application include optimization, constraint handling, machine learning, and multi-ob<x>jective optimization.

Description

Course keywords: 演化計算 Evolutionary Computation, 人工智慧 Artificial Intelligence, 計算智慧 Computational Intelligence, 基因演算法 Genetic Algorithm, 演化策略 Evolution Strategies, 遺傳規劃 Genetic Programming, 最佳化 Optimization 一、課程說明 (Course Description) This course provides an introduction to the algorithms, theory, and application of evolutionary computation geared towards the students who are interested in development and/or application of the subject. Topics in algorithms and theory include genetic algorithms, evolution strategies, genetic programming, parameter setting and analysis. Topics in application include optimization, constraint handling, machine learning, and multi-objective optimization. 二、指定用書 (Text Book) A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer Verlag, 2003/2010. 三、參考書籍 (References) 四、教學方式 (Teaching Method) Lecture slides and blackboard teaching 五、教學進度 (Syllabus) 1. Introduction 2. What is an Evolutionary Algorithm? 3. Genetic Algorithms 4. Evolution Strategies 5. Genetic Programming 6. Multi-objective Evolutionary Algorithms 7. Working with EAs 六、成績考核 (Evaluation) Assignment: 30% Midterm Exam: 40% Project: 30% 七、可連結之網頁位址 eeclass

Timetable

Scores

Average Percentage 81.21

Std. Deviation 10.54

Remarks

先修科目:Programming

Restrictions

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization