Playing around with Genetic Algorithms
The goal of this program is to get a population of 6, 8 bit binary strings to 0.
The fittest chromosome gets to breed twice, with the worst being unable to breed.
Random chance of mutation occurs once per generation, ensuring a solution does not get trapped on a local optimum.
Generation: 0 Average: 103
Generation: 1 Average: 67
Generation: 2 Average: 51
Generation: 3 Average: 42
Generation: 4 Average: 34
Generation: 5 Average: 28
Generation: 6 Average: 28
Generation: 7 Average: 28
Generation: 8 Average: 33
Generation: 9 Average: 28
Generation: 10 Average: 28
Generation: 11 Average: 27
Generation: 12 Average: 27
Generation: 13 Average: 32
Generation: 14 Average: 27
Generation: 15 Average: 27
Generation: 16 Average: 26
Generation: 17 Average: 27
Generation: 18 Average: 26
Generation: 19 Average: 25
Generation: 20 Average: 25
Generation: 21 Average: 25
Generation: 22 Average: 21
Generation: 23 Average: 18
Generation: 24 Average: 16
Generation: 25 Average: 13
Generation: 26 Average: 10
Generation: 27 Average: 8
Generation: 28 Average: 8
Generation: 29 Average: 8
Generation: 30 Average: 8
Generation: 31 Average: 8
Generation: 32 Average: 8
Generation: 33 Average: 6