Introduction to GAs, including motivation, simple ga mechanics, simple GA implementation, simple GA applications (2 weeks). GA theory, including schemata theory, building-block hypothesis, no-free-lunch theorem, GA takeover, building-block decision making, deceptions, and problem difficulty (3 weeks). GA-related issues, including different selection, crossover, and mutation operators, niching, problems with constraints, real-coded GAs, evolutionary strategies, and genetic programming (3 weeks). Current GA development, including competent GAs, estimation of distribution algorithms, and efficiency enhancement techniques (3 weeks).

(1) To make students have basic understanding of mechanisms, theory, and applications of genetic algorithms. (2) To train students to have the ability of applying genetic algorithms of their optimization problems.

Personality Prerequisite: Activeness, enthusiasm, and curiosity Knowledge Prerequisite: Probability Algorithms (optional)

Textbook: None. References: David E. Goldberg (1989). Genetic algorithms in search, optimization, and machine learning. ISBN: 0201157675 David A. Coley (1999). An Introduction to Genetic Algorithms for Scientists and Engineers. ISBN: 9810236026 David E. Goldberg (2002). The design of innovation: lessons from and for competent genetic algorithms. ISBN: 1402070985