1. Applications of Genetic Algorithms TJHSST Computer Systems Lab 2008-2009
By Mary Linnell

2. What is a Genetic Algorithm?
Evolutionary algorithm
Population consisting of individuals
Least fit individuals killed off
Best fit individuals bred with rest of population
2007/10/king_penguin_breeding_1sfw.jpg
A population of penguins

3. Genetic Algorithm Applications
Othello AI
N Queens Problem
Optimizing of Traveling Salesman Problem
Many other problems

4. Purpose and Goals Find minimum point of a three-dimensional graph
Testing every point would involve too many computations
Use genetic algorithms to simplify this problem

5. Purpose and Goals Vary the population size to see what is “best”
If too small
Population not representative of search space
Population will converge to a local minimum
Too many random mutations to find true solution
If too large
Long run times
Large amount of computer space and memory

6. Procedure and Methods
N randomly-generated yellow points, where N is the population size

7. Procedure and Methods
Lots of local minimums
Side view of graph

8. Original setup

9. 25% of population selected

10. Selected individuals removed

11. New individuals bred

12. New individuals become part of the population

13. Random mutation

14. After a single trial...

15. Found local minimums

16. After a lot of iterations, random mutation helps

17. Results of Multiple Trials
Population size 8 16 32 64
True z-value
Average result
Difference

18. Results of Multiple Trials

19. Future Study Improved algorithm to avoid local minima
Change other parameters of genetic algorithm