Exploring Genetic Algorithms Through the Iterative Prisoner's Dilemma Computer Systems Lab 2007-2008 Aaron Dufour

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

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

Recombination DoublePoint SinglePoint 10010101 10010101 01000011 Yields 10000011 01010101 DoublePoint 10010101 01000011 Yields 10000001 01010111

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

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

Data Analysis

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

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

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

Analysis Tool

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%)‏