import Initializer, Mutator, Recombiner, Selector, Replacer, Terminator, LocalSearcher
from GeneticAlgorithm import GeneticAlgorithm
from ProblemDefinition import ProblemDefinition
from Benchmark import Benchmark
# Choose your operators
initializer = Initializer.RandomInitializer()
mutator = Mutator.RandomMutator(0.2, dynAdapt=True)
recombiner = Recombiner.CrossoverRecombiner()
selector = Selector.TournamentSelector(s=20, dynAdapt=True)
replacer = Replacer.bottomReplacer()
terminator = Terminator.maxRuntimeTerminator(10)
# Add a local searcher if you want LocalSearcher.Idle() does nothing
localSearcher = LocalSearcher.Idle()
# Set up an example problem
nrMachines, jobRuntimes = Benchmark.benchmark1()
probDef = ProblemDefinition(nrMachines, jobRuntimes)
# Set up GA parameters
popSize = 100
nrOffspring = int(popSize / 10)
# Create Genetic Algorithm instance
GA = GeneticAlgorithm(initializer, selector, recombiner, mutator, replacer,
terminator, probDef, popSize, nrOffspring, localSearcher)
bestIndividual, results = GA.run()
print("best Individuals fitness: ", bestIndividual.fitness)
