Tutorial 4: Prepare the Population Configuration - ci-group/revolve GitHub Wiki

Here we will learn how to repare the Population Configuration for an experiment.

1. Within the project root create an empty file tutorial4.py.
2. Make sure it is executable:

   sudo chmod +x tutorial4.py
   

3. Open the file with a text editor, add this piece of a code to the script and save it

#!/usr/bin/env python3
from pyrevolve import parser
from pyrevolve.evolution import fitness
from pyrevolve.evolution.selection import multiple_selection, tournament_selection
from pyrevolve.evolution.population import Population, PopulationConfig
from pyrevolve.evolution.pop_management.steady_state import steady_state_population_management
from pyrevolve.experiment_management import ExperimentManagement
from pyrevolve.genotype.plasticoding.crossover.crossover import CrossoverConfig
from pyrevolve.genotype.plasticoding.crossover.standard_crossover import standard_crossover
from pyrevolve.genotype.plasticoding.initialization import random_initialization
from pyrevolve.genotype.plasticoding.mutation.mutation import MutationConfig
from pyrevolve.genotype.plasticoding.mutation.standard_mutation import standard_mutation
from pyrevolve.genotype.plasticoding.plasticoding import PlasticodingConfig


async def run():

    # Define the size
    population_size = 20
    offspring_size = 10

    # Define the maximum number of structural modules per robot
    genotype_conf = PlasticodingConfig(
        max_structural_modules=100
    )
    # Define the mutation probability
    mutation_conf = MutationConfig(
        mutation_prob=0.8,
        genotype_conf=genotype_conf,
    )
    # Define the Crossover probability
    crossover_conf = CrossoverConfig(
        crossover_prob=0.8,
    )

    # Parse command line / file input arguments
    settings = parser.parse_args()
    experiment_management = ExperimentManagement(settings)

    # Insert the population parameters PopulationConfig:
    #   Creates a PopulationConfig object that sets the particular configuration for the population
    #
    #         :param population_size: size of the population
    #         :param genotype_constructor: class of the genotype used
    #         :param genotype_conf: configuration for genotype constructor
    #         :param fitness_function: function that takes in a `RobotManager` as a parameter and produces a fitness for the robot
    #         :param mutation_operator: operator to be used in mutation
    #         :param mutation_conf: configuration for mutation operator
    #         :param crossover_operator: operator to be used in crossover
    #         :param selection: selection type
    #         :param parent_selection: selection type during parent selection
    #         :param population_management: type of population management ie. steady state or generational
    #         :param evaluation_time: duration of an experiment
    #         :param experiment_name: name for the folder of the current experiment
    #         :param experiment_management: object with methods for managing the current experiment
    #         :param offspring_size (optional): size of offspring (for steady state)
    population_conf = PopulationConfig(
        population_size=population_size,
        genotype_constructor=random_initialization,
        genotype_conf=genotype_conf,
        fitness_function=fitness.displacement_velocity,
        mutation_operator=standard_mutation,
        mutation_conf=mutation_conf,
        crossover_operator=standard_crossover,
        crossover_conf=crossover_conf,
        selection=lambda individuals: tournament_selection(individuals, 2),
        parent_selection=lambda individuals: multiple_selection(individuals, 2, tournament_selection),
        population_management=steady_state_population_management,
        population_management_selector=tournament_selection,
        evaluation_time=settings.evaluation_time,
        offspring_size=offspring_size,
        experiment_name=settings.experiment_name,
        experiment_management=experiment_management
    )

4. Run the script:

   (.venv) ./revolve.py --simulator-cmd=gazebo --manager ./tutorial4.py
   

You should get an output like this:

STARTING
FINISHED

---

See next: Tutorial 5: Set up experiments

---