Eight Queens with Evolutionary Computing

Sample evocom output:
coruh-imac:evocom ergun$ python evocom.py -r 100 -b 20
evocom started.
===============================
Average iterations = 507
Total failures = 0
population = 100
trials = 10000
runs = 100
best fits = 20
mutation probability= 80%
select from = 5
===============================
5.2 Effects of Mutations
Mutations are necessary to drive diversity in a given population. Recombinations
(crossing-over) of parent genes would not be enough for better adaptation.
It should be noted that the graph given in Figure 4 should not be taken as
a universal benchmark as mutations are complex phenomena with many nonlinear
parameters in effect.
Using evocom we recorded number of iterations vs. mutation probabilities
while searching for 10 bets-fit individuals in a population of 100. The mutation
probability was specified with -m command line option (-m 80 means 80 percent
mutation probability).
Figure 4: Mutation probability graph
10