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

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Presentation on theme: "Artificial Intelligence Genetic Algorithms Source: www.myreaders.info 1."— Presentation transcript:

1 Artificial Intelligence Genetic Algorithms Source: 1

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. 2

3 Genetic Algorithms 3

4 4 Why Genetic Algorithms?

5 Optimization 5

6 6

7 Search Optimization Algorithms 7

8 8

9 Biological Background – Basic Genetics 9

10 10

11 Biological Background – Basic Genetics 11

12 Biological Background – Basic Genetics 12

13 Biological Background – Basic Genetics 13

14 Biological Background – Basic Genetics 14

15 Search Space 15

16 Working Principles 16

17 Working Principles 17

18 Outline of Basic Genetic Algorithm 18

19 Outline of Basic Genetic Algorithm 19

20 20

21 Encoding- Genetic Algorithms 21

22 Encoding- Genetic Algorithms 22

23 Binary Encoding 23

24 Binary Encoding 24

25 25

26 26

27 Value Encoding 27

28 Permutation Encoding 28

29 Permutation Encoding 29

30 Tree Encoding 30

31 Tree Encoding 31

32 Operators of Genetic Algorithm 32

33 Operators of Genetic Algorithm 33

34 Reproduction – or Selection 34

35 Reproduction – or Selection 35

36 Reproduction – or Selection 36

37 Example of Selection 37

38 38

39 Roulette Wheel Selection 39

40 Roulette Wheel Selection 40

41 Roulette Wheel Selection 41

42 Boltzmann Selection 42

43 Boltzmann Selection 43

44 Crossover operator 44

45 One-point Crossover 45

46 Two-point Crossover 46

47 Uniform Crossover 47

48 Arithmetic Crossover 48

49 Heuristic Crossover 49

50 Mutation 50

51 Mutation 51

52 Flip Bit 52

53 Boundary 53

54 Non Uniform 54

55 Uniform 55

56 Gaussian 56

57 Examples 57

58 Genetic Algorithm Approach to problem Maximize f(x)=x2 58

59 Genetic Algorithm Approach to problem Maximize f(x)=x2 59

60 Genetic Algorithm Approach to problem Maximize f(x)=x2 60

61 Genetic Algorithm Approach to problem Maximize f(x)=x2 61

62 Genetic Algorithm Approach to problem Maximize f(x)=x2 62

63 Genetic Algorithm Approach to problem Maximize f(x)=x2 63

64 Genetic Algorithm Approach to problem Maximize f(x)=x2 64


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