1. Exploring Genetic Algorithms Through the Iterative Prisoner's Dilemma
Computer Systems Lab
Aaron Dufour

4. Mutation Rate How the mutation rate changes per generation
Within a generation the mutation rate does not change

5. Natural Selection
How many are removed from the population each generation
Static rate – the same number are removed each generation
Fitness-based rate – all those below a threshold fitness value are removed

6. Recombination DoublePoint SinglePoint 10010101 10010101 01000011
Yields
DoublePoint
Yields

7. Initial Population Creation
Simple
Random binary
Flip Half
Random on first half
Second half is inverted first half
Ensures that every bit has 50% 1's and 50% 0's
Check for Duplicates
Same as flip half, except remakes each one that has a duplicate
Ensures that all of the solutions are different

8. Output Outputs the average fitness value for each generation
File name is “g i p t s m n r f.txt”
g – number of generations
i – number of iterations
p – population size
t – number of turns
s – initial population type
m – mutation rate info
n – natural selection info
r – recombination type
f – test number
Example – s s s-0.5 s t0.txt

9. Data Analysis

10. Data Analysis, cont’d
The program analyzes the data to find where the fitness stabilizes
Although we can do this visually, it is difficult for the computer
My algorithm eliminates data from the left side until the slope of a fit line gets within a certain amount of 0

11. Final Product
The program automatically loops through each of the algorithms for each method that can change, as well as certain values of variables that I chose based on many trials
It does 10 runs with each setting, and outputs the number of generations each one took in addition to an average

12. Analysis Tool
Graphs the number of iterations that each run with a specific attribute took
First input line is number of graphs
The following (one for each graph) are a number followed by a string
A second program moved files between two folders so I could eliminate those with attributes that did not perform well

13. Analysis Tool

14. Analysis
I could not make any conclusions about the mutation rate or initial population creation
The double-point recombination slightly outperformed the single-point recombination algorithm
The fitness-based natural selection was better than the static natural selection, and got better with a higher percent of the maximum required (up to 95%)‏