def main():
# Creating function optimalization model implementing genetic algorithm
opt = Optimizer(target=function,
epochs=EPOCHS,
args_num=N,
x1_range=X1_RANGE,
x2_range=X2_RANGE,
precision=PRECISION,
population_size=POPULATION_SIZE,
type="min")
# Initiation of pupulation
opt.initPopulation()
print(opt.population)
# TEST : Decoding population's chtomosomes
for x in opt.population:
print(x.decode(), ", ", opt.target(x.decode()))
# Evaluating current population
opt.evaluate()
# TEST : Selecting parents
# Truncation
best = opt.truncation(2)
print("TRUNCATION : best = ", best, " decimal : ", best.decode(),
"target : ", best.getTargetValue())
# Tournament
best = opt.tournament(0.3)
print("TOURNAMENT : best = ", best, " decimal : ", best.decode(),
"target : ", best.getTargetValue())
# Roulette
opt.normalizePopulation()
best = opt.roulette()
print("Roulette : best = ", best, " decimal : ", best.decode(),
"target : ", best.getTargetValue())
# Mutation
print("MUTATION : ",
opt.mutate(Chromosome([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 10), 2, 1))
print("MUTATION EDGE : ",
opt.mutateEdge(Chromosome([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 10), 2, 1))
# Generating next generation with truncation
# print("old pop : ", opt.population)
# print("old pop decimal : ", [x.getTargetValue() for x in opt.population])
# opt.nextGen("tournament", 5)
# print("new pop : ", opt.population)
# print("new pop decimal : ", [x.getTargetValue() for x in opt.population])
# Proper optimalization of target function
# opt.initPopulation()
# print("~~~~~~~~~~~~~ OPTIMALIZATION ~~~~~~~~~~~~~~~")
# print("old pop : ", opt.population)
# opt.evaluate()
# print("old pop decimal : ", [x.getTargetValue() for x in opt.population])
# opt.optimize()
# print("new pop : ", opt.population)
# print("new pop decimal : ", [x.getTargetValue() for x in opt.population])
# GENEREAL TEST
opt_test = Optimizer(target=function,
epochs=EPOCHS,
args_num=N,
x1_range=X1_RANGE,
x2_range=X2_RANGE,
precision=PRECISION,
population_size=POPULATION_SIZE,
type="min")
opt_test.initPopulation()
opt_test.optimize()
print("best = ", opt_test.getBest().getTargetValue())
