A quick introduction to the Genetic Algorithm for computer science students, with descriptive definitions and two authentic examples.

1. Introduction to the Genetic
Algorithm
Qiang Hao
Learning, Design and Technology & Computer Science
University of Georgia

2. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.

3. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Purpose: To generate useful solutions to optimization and search
problems.
● Reasons: Searching space is gigantically huge.

4. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:

5. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation

6. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation

7. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation

8. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation
Original: A, T, C, G, U
Afterwards:
A, A, C, G, U

9. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation
Loop

10. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● Natural Selection:
a. Have an initial population
b. Selection
c. Crossover and mutation
Loop

11. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation

12. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation
d. Termination

13. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation
d. Termination
1. A genetic representation of the
solution domain
2. A fitness function to evaluate
the solution domain

14. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● GA:
1. A genetic representation of the solution domain
2. A fitness function to evaluate the solution domain
3. Have an initial population
4. Selection
5. Crossover and mutation
6. Termination
Loop

15. Definition
The genetic algorithm (GA) is a search heuristic that mimics the process of natural
selection.
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation
d. Termination

16. Example
Multiple fault diagnosis
http://bit.ly/1SVHsNJ
Potter, W. D., Miller, J. A., Tonn, B. E., Gandham, R. V., & Lapena, C. N. (1992).
Improving the reliability of heuristic multiple fault diagnosis via the EC-based
genetic algorithm. Applied Intelligence, 2(1), 5-23.

17. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
1. We limit the total diagnosable manifestations to 10.
2. These 10 manifestations are associated with 15 diseases.

18. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
● Step 1 - Bit representation:
○ {1, 0, 1, 0, 1, 1, 1, 1, 0, 1} -- manifestation
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination

19. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
● Step 2 - Fitness Function:
○ {1, 0, 1, 0, 1, 1, 1, 1, 0, 1} -- manifestation
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0} -- disease combination
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0} -- disease combination

20. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
● Step 2 - Fitness Function:

21. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
● Step 3 - Have an initial population
600 random disease combinations
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0} -- disease combination
○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0} -- disease combination

22. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
Step 4 - Selection
● Tournament Selection
● Roulette wheel selection

23. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
Step 4 - Selection
● Tournament Selection
a. choose k individuals from the population at random
b. choose the best individual from pool
Population size: 600; tournament size: 6; repetition times: 600

24. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
Step 4 - Selection
● Roulette wheel selection

25. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
Step 4 - Selection
● Roulette wheel selection

26. Example
Step 4 - Selection
● Roulette wheel selection

27. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
Step 5 - Crossover
● One-point xover:
● Two-point xover:

28. Example
Multiple fault diagnosis
Given a set of manifestations, can you automatically determine what diseases
cause this set of manifestations with acceptable accuracy?
3. Have an initial population
4. Selection
5. Crossover and mutation
6. Termination
Loop

29. Example 2
Forest Planning Optimization
http://bit.ly/1OmAxxn
Potter, W. D., Drucker, E., Bettinger, P., Maier, F., Martin, M., Luper, D., ... &
Hayes, C. (2009). Diagnosis, configuration, planning, and pathfinding:
Experiments in nature-inspired optimization. In Natural Intelligence for
Scheduling, Planning and Packing Problems (pp. 267-294). Springer Berlin
Heidelberg.

30. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
1. Three cutting seasons per year
2. Two adjacent fields can not both be cutted in one season

31. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation
d. Termination
1. A genetic representation of the
solution domain
2. A fitness function to evaluate
the solution domain

32. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
● A genetic representation of the solution domain:
[0, 2, 1, 3, 1 …...1, 0, 0, 1, 3, 2]

33. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
● A genetic representation of the solution domain:
[0, 2, 1, 3, 1 …...1, 0, 0, 1, 3, 2]
● Fitness Function:
(Output of season 1 - target)2
+ (Output of season 2 - target)2
+ (Output of
season 3 - target)2

34. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
● A genetic representation of the solution domain:
[0, 2, 1, 3, 1 …...1, 0, 0, 1, 3, 2]
● Fitness Function:
(Output of season 1 - target)2
+ (Output of season 2 - target)2
+ (Output of
season 3 - target)2

35. Example 2
Forest Planning Optimization
Given a forest composed of 73 adjacent fields, what cutting schedule would make
the seasonal wood production closest to a fixed certain number?
● GA:
a. Have an initial population
b. Selection
c. Crossover and mutation
d. Termination

36. Thanks.