Eight Queens with Evolutionary Computing

Eight Queens Algorithm Description
Representation Permutations
Recombination ”Cut-and-crossfill” crossover
Recombination probability 100 pc
Mutation Swap
Mutation probability 80 pc
Parent selection Best 2 out of random 5
Survival selection Replace worst
Population size 100
Number of offspring 2
Initialisation Random
Termination condition Solution of 10,000 fitness evaluation
4.3 Evocom
The author has written a computer program called evocom and implemented
the evolutionary algorithm given in Section 3.7. Evocom is a Python command
line application that has various options to adjust configuration parameters.
coruh-imac:evocom ergun$ python evocom.py -h
evocom started.
Usage: evocom.py [options]
Options:
-h, --help show this help message and exit
-p POPULATION, --population=POPULATION
-t TRIALS, --trials=TRIALS
-r RUNS, --runs=RUNS
-b BESTFITS, --bestfits=BESTFITS
-m MUTATION, --mutation=MUTATION
-s SELECTFROM, --selectfrom=SELECTFROM
-v, --verbose
Population: Initial size of the population. Default is 100.
Trials: Maximum iterations to terminate the program in case search fails.
Default is 10000.
Runs: Number of runs. This parameter is needed to find the average performance
as each run finishes in different number of iterations. Default is 10.
Best fits: Number of fittest individuals to find before breaking the iteration.
Default is 5.
Select from: Number of parents randomly chosen from the population to pick
the fittest two to mate. Default is 5.
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