1. Introduction to Genetic Algorithm Principle: survival-of-the-fitness Characteristics of GA Robust Error-tolerant Flexible When you have no idea about solving problems …

2. ........... CrossoverMutation Crossover Mutation Population Selection Fitness

3. Component of Genetic Algorithm Representation Genetic operations: Crossover, mutation,inversion, as you wish Selection Elitism, total, steady state, … as you wish Fitness Problem dependent Everybody has different survival approaches.

4. How to implement a GA ? Representation Fitness Operators design Selection strategy

5. Example(I) Maximize

7. Example(I): Representation Standard GA  binary string x = 5,  x = 101 x = 3.25  x = 011.1 … Something noticeable Length is predefined. Not the only way. chromosome gene

8. Example(I): Fitness function In this case, it is known already

9. Example(I): Genetic Operator Standard crossover (one-point crossover)

10. Example(I): Genetic Operator Standard mutation (point mutation) Randomly

11. Example(I): Selection Standard selection (roulette wheel)

12. ........... CrossoverMutation Crossover Mutation Population Selection Fitness

14. Example(II) Minimize

15. 繪圖中 嘿嘿～～畫不出來

16. Example(II): Representation Standard GA  binary string Too complex Intuitively Real numbered coding

17. Example(II): Fitness function In this case, it is known already

18. Example(II): Genetic Operator Standard crossover (multi-point crossover) 0.3, 0.7, 1.2, 3.5, -9.87, 2.334, 34 0.1, 0.3, -1, 2.5, 1.33, 0.434, 9 0.1, 0.3, 1.2, 3.5, 1.33, 0.434, 9 0.3, 0.7, -1, 2.5, -9.87, 2.334, 34

19. Example(II): Genetic Operator Standard mutation (multi-point mutation) 0.3, 0.7, 1.2, 3.5, -9.87, 2.334, 34 0.3, 0.7, 0.9, 3.5, -9.87, 2.557, 34 Cauchy Gaussian

20. Example(II): Selection Standard selection (rank selection)

21. Comparing Two Selection

22. ........... CrossoverMutation Crossover Mutation Population Selection Fitness