Download presentation

Presentation is loading. Please wait.

Published byChris Duffield Modified over 2 years ago

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

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google