1. Exploring Genetic Algorithms Through the Iterative Prisoner's Dilemma Computer Systems Lab 2007-2008 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 SinglePoint 10010101 01000011 Yields 10000011 01010101 DoublePoint 10010101 01000011 Yields 10000001 01010111

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 – 10 100 150 s s-0.0050 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. Next Quarter  Next quarter I will automate the process of creating data and then finding the stabilization point  Then I can use the results to come to a conclusion about the different methods