if __name__ == "__main__":
# my_optimization.run(max_hours_runtime=MAX_TIME, max_gens=MAX_GENS, num_random_individuals=NUM_RANDOM_INDS,
# directory=RUN_DIR, name=RUN_NAME, max_eval_time=MAX_EVAL_TIME,
# time_to_try_again=TIME_TO_TRY_AGAIN, checkpoint_every=CHECKPOINT_EVERY,
# save_vxa_every=SAVE_POPULATION_EVERY, save_lineages=SAVE_LINEAGES)
# Here is how to use the checkpointing mechanism
if not os.path.isfile("./" + RUN_DIR + "/pickledPops/Gen_0.pickle"):
# start optimization
my_optimization.run(max_hours_runtime=MAX_TIME,
max_gens=MAX_GENS,
num_random_individuals=NUM_RANDOM_INDS,
directory=RUN_DIR,
name=RUN_NAME,
max_eval_time=MAX_EVAL_TIME,
time_to_try_again=TIME_TO_TRY_AGAIN,
checkpoint_every=CHECKPOINT_EVERY,
save_vxa_every=SAVE_POPULATION_EVERY,
save_lineages=SAVE_LINEAGES)
else:
continue_from_checkpoint(directory=RUN_DIR,
additional_gens=EXTRA_GENS,
max_hours_runtime=MAX_TIME,
max_eval_time=MAX_EVAL_TIME,
time_to_try_again=TIME_TO_TRY_AGAIN,
checkpoint_every=CHECKPOINT_EVERY,
save_vxa_every=SAVE_POPULATION_EVERY,
save_lineages=SAVE_LINEAGES)
my_objective_dict = ObjectiveDict()
my_objective_dict.add_objective(name="fitness",
maximize=True,
tag=FITNESS_TAG)
my_objective_dict.add_objective(name="age", maximize=False, tag=None)
my_pop = Population(my_objective_dict,
MyGenotype,
Phenotype,
pop_size=POP_SIZE)
my_optimization = ParetoOptimization(my_sim, my_env, my_pop)
my_optimization.run(max_hours_runtime=MAX_TIME,
max_gens=MAX_GENS,
num_random_individuals=NUM_RANDOM_INDS,
directory=RUN_DIR,
name=RUN_NAME,
max_eval_time=MAX_EVAL_TIME,
time_to_try_again=TIME_TO_TRY_AGAIN,
checkpoint_every=CHECKPOINT_EVERY,
save_vxa_every=SAVE_VXA_EVERY)
else:
continue_from_checkpoint(directory=RUN_DIR,
max_hours_runtime=MAX_TIME,
max_eval_time=MAX_EVAL_TIME,
time_to_try_again=TIME_TO_TRY_AGAIN,
checkpoint_every=CHECKPOINT_EVERY,
save_vxa_every=SAVE_VXA_EVERY)
