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)