names = ["Unique name", "Parameter a", "Parameter b"] new_parameters_table = tables.new(data, names) # ----------------------------------------------------------------- # Save the new parameters table tables.write(new_parameters_table, new_parameters_path, format="ascii.ecsv") # Dump the GA ga.saveto(new_path) # ----------------------------------------------------------------- # Save the state of the random generator new_random_path = fs.join(new_generation_path, "rndstate.pickle") save_state(new_random_path) # ----------------------------------------------------------------- # Initialize evolution: # self.initialize() # done in 'explore' # self.internalPop.evaluate() # self.internalPop.sort() # Evaluate function of population is just loop over evaluate function of individuals # Evaluate function of individual (genome) is just calculating the sum of scores of each target function # newpop = ga.generate_new_population() # for ind in newpop: print(ind.genomeList)
for ind in pop: # Give the individual a unique name name = time.unique_name(precision="micro") name_column.append(name) par_a_column.append(ind.genomeList[0]) par_b_column.append(ind.genomeList[1]) # Create the parameters table data = [name_column, par_a_column, par_b_column] names = ["Unique name", "Parameter a", "Parameter b"] parameters_table = tables.new(data, names) # Save the genetic algorithm ga.saveto(path) #print("Current generation: ", ga.currentGeneration) # Save the parameter table tables.write(parameters_table, parameters_path, format="ascii.ecsv") # ----------------------------------------------------------------- # Path to the random state random_path = fs.join(fs.cwd(), "rndstate.pickle") # Save the state of the random generator save_state(random_path) # -----------------------------------------------------------------
names = ["Unique name", "Parameter a", "Parameter b"] new_parameters_table = tables.new(data, names) # ----------------------------------------------------------------- # Save the new parameters table tables.write(new_parameters_table, new_parameters_path, format="ascii.ecsv") # Dump the GA ga.saveto(new_path) # ----------------------------------------------------------------- # Save the state of the random generator new_random_path = fs.join(new_generation_path, "rndstate.pickle") save_state(new_random_path) # ----------------------------------------------------------------- # Initialize evolution: # self.initialize() # done in 'explore' # self.internalPop.evaluate() # self.internalPop.sort() # Evaluate function of population is just loop over evaluate function of individuals # Evaluate function of individual (genome) is just calculating the sum of scores of each target function # newpop = ga.generate_new_population() # for ind in newpop: print(ind.genomeList) #pop = ga.get_population()