Artificial Intelligence

1. Artificial Intelligence Genetic Algorithms Source: www.myreaders.info

2. Genetic Algorithms • Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. • Genetic algorithms are inspired by Darwin's theory about evolution. • It is an intelligent random search technique used to solve optimization problem • Although randomized but GA’s exploit historical information to direct the search into the region of better performance within the search space.

3. Genetic Algorithms

4. Why Genetic Algorithms?

5. Optimization

6. Optimization

7. Search Optimization Algorithms

8. Search Optimization Algorithms

9. Biological Background – Basic Genetics

10. Biological Background – Basic Genetics

11. Biological Background – Basic Genetics

12. Biological Background – Basic Genetics

13. Biological Background – Basic Genetics

14. Biological Background – Basic Genetics

15. Search Space

16. Working Principles

17. Working Principles

18. Outline of Basic Genetic Algorithm

19. Outline of Basic Genetic Algorithm

21. Encoding- Genetic Algorithms

22. Encoding- Genetic Algorithms

23. Binary Encoding

24. Binary Encoding

27. Value Encoding

28. Permutation Encoding

29. Permutation Encoding

30. Tree Encoding

31. Tree Encoding

32. Operators of Genetic Algorithm

33. Operators of Genetic Algorithm

34. Reproduction – or Selection

35. Reproduction – or Selection

36. Reproduction – or Selection

37. Example of Selection

39. Roulette Wheel Selection

40. Roulette Wheel Selection

41. Roulette Wheel Selection

42. Boltzmann Selection

43. Boltzmann Selection

44. Crossover operator

45. One-point Crossover

46. Two-point Crossover

47. Uniform Crossover

48. Arithmetic Crossover

49. Heuristic Crossover

50. Mutation