accentuation

...light out... I spent the morning trying to figure out a way to single out the fitnesses of the most fit of the first generation using python. Doing so will answer some questions...like how good can one individual be? or what effect does the topology that the system is run on have on the same individuals.... Because the way my program is set up, so long as no files have been deleted all runs will start off with the same first generation... in one way or another, if the user chooses different seeded rate then the numbers will shift, but for all the experiment all the parameters where the same except for the mutation rate... Because of this all the runs have the exact same first generation. Except for the fitnesses all the logs look like the first generation has been copied and pasted. The program was kinda hard for me. In the end python was smaller, only like 43 lines of code and the first half was just imports. Arrays and indices are a little complex for me to use fluidly right now but im getting there. Anyway I made charts of 2 different typologies and the results are interesting.. to me at least .

Joey to Joey 

Joey to Joey

Joey to Utah

Joey to Utah

The "Joey to Joey" plots show the same trend. the first box... actually both one and two have the same look to them, so does three... There is little difference in either "joey to joey", this make sense because it is the same individuals on the same topology. But in terms of meaning the mean fitness is really close. There are bound to be stragglers but the fitness tends to be the same... or at least in the same area. The trend can be seen when comparing both "Joey to 'Utah'". But when it comes to comparing JTJ... Joey to joey... and JTU... joey to utah... there is a difference. In JTU all the boxes are notably smaller then there correspondents in JTJ, meaning that across the internet the fitness of an individual is more likely to be close to the true fitness.