From 4d913250e358f3fab4db0084904b6d8fdbcd2895 Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Wed, 19 Feb 2025 15:04:26 +0100
Subject: [PATCH 1/7] added .individuals to offspring and parents where
 relevant in example 4b

---
 .../4b_simple_ea_xor_database/main.py               | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/examples/4_example_experiment_setups/4b_simple_ea_xor_database/main.py b/examples/4_example_experiment_setups/4b_simple_ea_xor_database/main.py
index 811456ba5..ffc576cb3 100644
--- a/examples/4_example_experiment_setups/4b_simple_ea_xor_database/main.py
+++ b/examples/4_example_experiment_setups/4b_simple_ea_xor_database/main.py
@@ -91,8 +91,8 @@ def select(
         original_survivors, offspring_survivors = population_management.steady_state(
             [i.genotype for i in population.individuals],
             [i.fitness for i in population.individuals],
-            [i.genotype for i in offspring],
-            [i.fitness for i in offspring],
+            [i.genotype for i in offspring.individuals],
+            [i.fitness for i in offspring.individuals],
             lambda n, genotypes, fitnesses: selection.multiple_unique(
                 n,
                 genotypes,
@@ -112,8 +112,8 @@ def select(
                 ]
                 + [
                     Individual(
-                        genotype=offspring[i].genotype,
-                        fitness=offspring[i].fitness,
+                        genotype=offspring.individuals[i].genotype,
+                        fitness=offspring.individuals[i].fitness,
                     )
                     for i in offspring_survivors
                 ]
@@ -145,10 +145,11 @@ def reproduce(self, population: NDArray[np.int_], **kwargs: Any) -> list[Genotyp
         )  # We select the population of parents that were passed in KWArgs of the parent selector object.
         if parents is None:
             raise KeyError("No children passed.")
+        #print(type(parents))
         offspring = [
             Genotype.crossover(
-                parents[parent1_i].genotype,
-                parents[parent2_i].genotype,
+                parents.individuals[parent1_i].genotype,
+                parents.individuals[parent2_i].genotype,
                 self._rng,
                 num_parameters=config.NUM_PARAMETERS,
             ).mutate(

From 96bb70360fac15f08b024c6661086b660e6b212d Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Wed, 19 Feb 2025 15:30:49 +0100
Subject: [PATCH 2/7] further abstracted isinstance assertion (np.int_ to
 np.integer) in _body_develop.py so that example 4c works

---
 .../genotypes/cppnwin/modular_robot/v2/_body_develop.py         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/standards/revolve2/standards/genotypes/cppnwin/modular_robot/v2/_body_develop.py b/standards/revolve2/standards/genotypes/cppnwin/modular_robot/v2/_body_develop.py
index f65a92c96..2b8996479 100644
--- a/standards/revolve2/standards/genotypes/cppnwin/modular_robot/v2/_body_develop.py
+++ b/standards/revolve2/standards/genotypes/cppnwin/modular_robot/v2/_body_develop.py
@@ -101,7 +101,7 @@ def __evaluate_cppn(
     """
     x, y, z = position
     assert isinstance(
-        x, np.int_
+        x, np.integer
     ), f"Error: The position is not of type int. Type: {type(x)}."
     body_net.Input([1.0, x, y, z, chain_length])  # 1.0 is the bias input
     body_net.ActivateAllLayers()

From f909f97af98b9d8be1120395baf64c8f779618ca Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Fri, 14 Mar 2025 19:18:00 +0100
Subject: [PATCH 3/7] added prototype project

---
 prototype/config.py     |   6 ++
 prototype/ea.py         | 160 ++++++++++++++++++++++++++++++++++++++++
 prototype/genotype.py   |  88 ++++++++++++++++++++++
 prototype/individual.py |  13 ++++
 prototype/main.py       | 104 ++++++++++++++++++++++++++
 prototype/rerun.py      |  28 +++++++
 prototype/tools.py      |  18 +++++
 7 files changed, 417 insertions(+)
 create mode 100644 prototype/config.py
 create mode 100644 prototype/ea.py
 create mode 100644 prototype/genotype.py
 create mode 100644 prototype/individual.py
 create mode 100644 prototype/main.py
 create mode 100644 prototype/rerun.py
 create mode 100644 prototype/tools.py

diff --git a/prototype/config.py b/prototype/config.py
new file mode 100644
index 000000000..59ff95885
--- /dev/null
+++ b/prototype/config.py
@@ -0,0 +1,6 @@
+"""Configuration parameters for this example."""
+
+NUM_SIMULATORS = 2 # should be number of cores
+POPULATION_SIZE = 100
+OFFSPRING_SIZE = 50
+NUM_GENERATIONS = 100
diff --git a/prototype/ea.py b/prototype/ea.py
new file mode 100644
index 000000000..aad8ac67a
--- /dev/null
+++ b/prototype/ea.py
@@ -0,0 +1,160 @@
+from typing import Any
+import multineat
+import numpy as np
+import numpy.typing as npt
+from genotype import Genotype
+from individual import Individual
+from revolve2.experimentation.evolution.abstract_elements import Reproducer, Selector
+from revolve2.experimentation.optimization.ea import population_management, selection
+
+
+class ParentSelector(Selector):
+    """Selector class for parent selection."""
+
+    rng: np.random.Generator
+    offspring_size: int
+
+    def __init__(self, offspring_size: int, rng: np.random.Generator) -> None:
+        """
+        Initialize the parent selector.
+
+        :param offspring_size: The offspring size.
+        :param rng: The rng generator.
+        """
+        self.offspring_size = offspring_size
+        self.rng = rng
+
+    def select(
+        self, population: list[Individual], **kwargs: Any
+    ) -> tuple[npt.NDArray[np.int_], dict[str, list[Individual]]]:
+        """
+        Select the parents.
+
+        :param population: The population of robots.
+        :param kwargs: Other parameters.
+        :return: The parent pairs.
+        """
+        return np.array(
+            [
+                selection.multiple_unique(
+                    selection_size=2,
+                    population=[individual.genotype for individual in population],
+                    fitnesses=[individual.fitness for individual in population],
+                    selection_function=lambda _, fitnesses: selection.tournament(
+                        rng=self.rng, fitnesses=fitnesses, k=1
+                    ),
+                )
+                for _ in range(self.offspring_size)
+            ],
+        ), {"parent_population": population}
+
+
+class SurvivorSelector(Selector):
+    """Selector class for survivor selection."""
+
+    rng: np.random.Generator
+
+    def __init__(self, rng: np.random.Generator) -> None:
+        """
+        Initialize the parent selector.
+
+        :param rng: The rng generator.
+        """
+        self.rng = rng
+
+    def select(
+        self, population: list[Individual], **kwargs: Any
+    ) -> tuple[list[Individual], dict[str, Any]]:
+        """
+        Select survivors using a tournament.
+
+        :param population: The population the parents come from.
+        :param kwargs: The offspring, with key 'offspring_population'.
+        :returns: A newly created population.
+        :raises ValueError: If the population is empty.
+        """
+        offspring = kwargs.get("children")
+        offspring_fitness = kwargs.get("child_task_performance")
+        if offspring is None or offspring_fitness is None:
+            raise ValueError(
+                "No offspring was passed with positional argument 'children' and / or 'child_task_performance'."
+            )
+
+        original_survivors, offspring_survivors = population_management.steady_state(
+            old_genotypes=[i.genotype for i in population],
+            old_fitnesses=[i.fitness for i in population],
+            new_genotypes=offspring,
+            new_fitnesses=offspring_fitness,
+            selection_function=lambda n, genotypes, fitnesses: selection.multiple_unique(
+                selection_size=n,
+                population=genotypes,
+                fitnesses=fitnesses,
+                selection_function=lambda _, fitnesses: selection.tournament(
+                    rng=self.rng, fitnesses=fitnesses, k=2
+                ),
+            ),
+        )
+
+        return [
+            Individual(
+                population[i].genotype,
+                population[i].fitness,
+            )
+            for i in original_survivors
+        ] + [
+            Individual(
+                offspring[i],
+                offspring_fitness[i],
+            )
+            for i in offspring_survivors
+        ], {}
+
+
+class CrossoverReproducer(Reproducer):
+    """A simple crossover reproducer using multineat."""
+
+    rng: np.random.Generator
+    innov_db_body: multineat.InnovationDatabase
+    innov_db_brain: multineat.InnovationDatabase
+
+    def __init__(
+        self,
+        rng: np.random.Generator,
+        innov_db_body: multineat.InnovationDatabase,
+        innov_db_brain: multineat.InnovationDatabase,
+    ):
+        """
+        Initialize the reproducer.
+
+        :param rng: The ranfom generator.
+        :param innov_db_body: The innovation database for the body.
+        :param innov_db_brain: The innovation database for the brain.
+        """
+        self.rng = rng
+        self.innov_db_body = innov_db_body
+        self.innov_db_brain = innov_db_brain
+
+    def reproduce(
+        self, population: npt.NDArray[np.int_], **kwargs: Any
+    ) -> list[Genotype]:
+        """
+        Reproduce the population by crossover.
+
+        :param population: The parent pairs.
+        :param kwargs: Additional keyword arguments.
+        :return: The genotypes of the children.
+        :raises ValueError: If the parent population is not passed as a kwarg `parent_population`.
+        """
+        parent_population: list[Individual] | None = kwargs.get("parent_population")
+        if parent_population is None:
+            raise ValueError("No parent population given.")
+
+        offspring_genotypes = [
+            Genotype.crossover(
+                parent_population[parent1_i].genotype,
+                parent_population[parent2_i].genotype,
+                self.rng,
+            ).mutate(self.innov_db_body, self.innov_db_brain, self.rng)
+            for parent1_i, parent2_i in population
+        ]
+        return offspring_genotypes
\ No newline at end of file
diff --git a/prototype/genotype.py b/prototype/genotype.py
new file mode 100644
index 000000000..38c9abfff
--- /dev/null
+++ b/prototype/genotype.py
@@ -0,0 +1,88 @@
+"""Genotype class."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import multineat
+import numpy as np
+
+from revolve2.modular_robot import ModularRobot
+from revolve2.standards.genotypes.cppnwin.modular_robot import BrainGenotypeCpg
+from revolve2.standards.genotypes.cppnwin.modular_robot.v2 import BodyGenotypeV2
+
+
+@dataclass
+class Genotype(BodyGenotypeV2, BrainGenotypeCpg):
+    """A genotype for a body and brain using CPPN."""
+
+    @classmethod
+    def random(
+        cls,
+        innov_db_body: multineat.InnovationDatabase,
+        innov_db_brain: multineat.InnovationDatabase,
+        rng: np.random.Generator,
+    ) -> Genotype:
+        """
+        Create a random genotype.
+
+        :param innov_db_body: Multineat innovation database for the body. See Multineat library.
+        :param innov_db_brain: Multineat innovation database for the brain. See Multineat library.
+        :param rng: Random number generator.
+        :returns: The created genotype.
+        """
+        body = cls.random_body(innov_db_body, rng)
+        brain = cls.random_brain(innov_db_brain, rng)
+
+        return Genotype(body=body.body, brain=brain.brain)
+
+    def mutate(
+        self,
+        innov_db_body: multineat.InnovationDatabase,
+        innov_db_brain: multineat.InnovationDatabase,
+        rng: np.random.Generator,
+    ) -> Genotype:
+        """
+        Mutate this genotype.
+
+        This genotype will not be changed; a mutated copy will be returned.
+
+        :param innov_db_body: Multineat innovation database for the body. See Multineat library.
+        :param innov_db_brain: Multineat innovation database for the brain. See Multineat library.
+        :param rng: Random number generator.
+        :returns: A mutated copy of the provided genotype.
+        """
+        body = self.mutate_body(innov_db_body, rng)
+        brain = self.mutate_brain(innov_db_brain, rng)
+
+        return Genotype(body=body.body, brain=brain.brain)
+
+    @classmethod
+    def crossover(
+        cls,
+        parent1: Genotype,
+        parent2: Genotype,
+        rng: np.random.Generator,
+    ) -> Genotype:
+        """
+        Perform crossover between two genotypes.
+
+        :param parent1: The first genotype.
+        :param parent2: The second genotype.
+        :param rng: Random number generator.
+        :returns: A newly created genotype.
+        """
+        body = cls.crossover_body(parent1, parent2, rng)
+        brain = cls.crossover_brain(parent1, parent2, rng)
+
+        return Genotype(body=body.body, brain=brain.brain)
+
+    def develop(self) -> ModularRobot:
+        """
+        Develop the genotype into a modular robot.
+
+        :returns: The created robot.
+        """
+        body = self.develop_body()
+        brain = self.develop_brain(body=body)
+        return ModularRobot(body=body, brain=brain)
diff --git a/prototype/individual.py b/prototype/individual.py
new file mode 100644
index 000000000..cb254b57f
--- /dev/null
+++ b/prototype/individual.py
@@ -0,0 +1,13 @@
+"""Individual class."""
+
+from dataclasses import dataclass
+
+from genotype import Genotype
+
+
+@dataclass
+class Individual:
+    """An individual in a population."""
+
+    genotype: Genotype
+    fitness: float
diff --git a/prototype/main.py b/prototype/main.py
new file mode 100644
index 000000000..1e9094c51
--- /dev/null
+++ b/prototype/main.py
@@ -0,0 +1,104 @@
+import logging
+import pickle
+from tqdm import tqdm
+
+import config
+import multineat
+from evaluator import Evaluator
+from genotype import Genotype
+from individual import Individual
+
+from revolve2.experimentation.evolution import ModularRobotEvolution
+from revolve2.experimentation.logging import setup_logging
+from revolve2.experimentation.rng import make_rng_time_seed
+from ea import ParentSelector, SurvivorSelector, CrossoverReproducer
+from tools import find_best_robot
+
+
+def run() -> None:
+    # Set up logging.
+    setup_logging(file_name="log.txt")
+
+    rng = make_rng_time_seed()
+
+    # initialize CPPN innovation databases (brain & body)
+    innov_db_body = multineat.InnovationDatabase()
+    innov_db_brain = multineat.InnovationDatabase()
+
+    """
+    - evaluator: Allows us to evaluate a population of modular robots.
+    - parent_selector: Allows us to select parents from a population of modular robots.
+    - survivor_selector: Allows us to select survivors from a population.
+    - crossover_reproducer: Allows us to generate offspring from parents.
+    - modular_robot_evolution: The evolutionary process as a object that can be iterated.
+    """
+    # initialize evolutionary process components
+    evaluator = Evaluator(headless=True, num_simulators=config.NUM_SIMULATORS)
+    parent_selector = ParentSelector(offspring_size=config.OFFSPRING_SIZE, rng=rng)
+    survivor_selector = SurvivorSelector(rng=rng)
+    crossover_reproducer = CrossoverReproducer(
+        rng=rng, innov_db_body=innov_db_body, innov_db_brain=innov_db_brain
+    )
+
+    modular_robot_evolution = ModularRobotEvolution(
+        parent_selection=parent_selector,
+        survivor_selection=survivor_selector,
+        evaluator=evaluator,
+        reproducer=crossover_reproducer,
+    )
+
+    # Create an initial population as we cant start from nothing.
+    logging.info("Generating initial population.")
+    initial_genotypes = [
+        Genotype.random(
+            innov_db_body=innov_db_body,
+            innov_db_brain=innov_db_brain,
+            rng=rng,
+        )
+        for _ in range(config.POPULATION_SIZE)
+    ]
+
+    # Evaluate the initial population.
+    logging.info("Evaluating initial population.")
+    initial_fitnesses = evaluator.evaluate(initial_genotypes)
+
+    # Create a population of individuals, combining genotype with fitness.
+    population = [
+        Individual(genotype, fitness)
+        for genotype, fitness in zip(initial_genotypes, initial_fitnesses, strict=True)
+    ]
+
+    # Save the best robot
+    best_robot = find_best_robot(None, population)
+
+    # Set the current generation to 0.
+    generation_index = 0
+
+    # Start the actual optimization process.
+    logging.info("Start optimization process.")
+    while generation_index < config.NUM_GENERATIONS:
+        logging.info(f"Generation {generation_index + 1} / {config.NUM_GENERATIONS}.")
+
+        """
+        In contrast to the previous example we do not explicitly stat the order of operations here, but let the ModularRobotEvolution object do the scheduling.
+        This does not give a performance boost, but is more readable and less prone to errors due to mixing up the order.
+
+        Not that you are not restricted to the classical ModularRobotEvolution object, since you can adjust the step function as you want.
+        """
+        population = modular_robot_evolution.step(
+            population
+        )  # Step the evolution forward.
+
+        # Find the new best robot
+        best_robot = find_best_robot(best_robot, population)
+
+        logging.info(f"Best robot until now: {best_robot.fitness}")
+        logging.info(f"Genotype pickle: {pickle.dumps(best_robot)!r}")
+
+        # Increase the generation index counter.
+        generation_index += 1
+
+if __name__ == "__main__":
+    run()
+
+
diff --git a/prototype/rerun.py b/prototype/rerun.py
new file mode 100644
index 000000000..e1858e26e
--- /dev/null
+++ b/prototype/rerun.py
@@ -0,0 +1,28 @@
+"""Rerun a robot with given body and parameters."""
+
+import logging
+import pickle
+
+from evaluator import Evaluator
+from individual import Individual
+
+from revolve2.experimentation.logging import setup_logging
+
+# This is a pickled genotype we optimized.
+# You can copy your own parameters from the optimization output log.
+PICKLED_GENOTYPE = b'\x80\x04\x95\x84\x13\x00\x00\x00\x00\x00\x00\x8c\nindividual\x94\x8c\nIndividual\x94\x93\x94)\x81\x94}\x94(\x8c\x08genotype\x94h\x05\x8c\x08Genotype\x94\x93\x94)\x81\x94}\x94(\x8c\x05brain\x94\x8cGrevolve2.standards.genotypes.cppnwin._multineat_genotype_pickle_wrapper\x94\x8c\x1eMultineatGenotypePickleWrapper\x94\x93\x94)\x81\x94X\xf1\x08\x00\x00{\n"value0":{\n"value0":0,\n"value1":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":2,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":8,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":9,\n"value10":1.0\n}\n],\n"value2":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":8,\n"value3":1,\n"value4":false,\n"value5":-0.4544869952184381\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":8,\n"value3":2,\n"value4":false,\n"value5":0.676048083864304\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":8,\n"value3":3,\n"value4":false,\n"value5":-0.5953685570925378\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":8,\n"value3":4,\n"value4":false,\n"value5":0.009840582764092401\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":8,\n"value3":5,\n"value4":false,\n"value5":0.5120174982690214\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":8,\n"value3":6,\n"value4":false,\n"value5":-0.7356904567062617\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":8,\n"value3":7,\n"value4":false,\n"value5":-0.5692049290039457\n}\n],\n"value3":7,\n"value4":1,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0.0,\n"value9":false,\n"value10":16384,\n"value11":{\n"value0":[]\n},\n"value12":8,\n"value13":7\n}\n}\x94b\x8c\x04body\x94h\r)\x81\x94X\xa7\t\x00\x00{\n"value0":{\n"value0":0,\n"value1":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":2,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":10,\n"value10":1.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":10,\n"value10":1.0\n}\n],\n"value2":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":6,\n"value3":1,\n"value4":false,\n"value5":0.5456837443101523\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":6,\n"value3":2,\n"value4":false,\n"value5":0.2225766151110867\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":6,\n"value3":3,\n"value4":false,\n"value5":0.13869819784354954\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":6,\n"value3":4,\n"value4":false,\n"value5":0.16110704268670845\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":6,\n"value3":5,\n"value4":false,\n"value5":0.044524845315174147\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":7,\n"value3":6,\n"value4":false,\n"value5":0.26762550975363866\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":7,\n"value3":7,\n"value4":false,\n"value5":0.3432962942367168\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":7,\n"value3":8,\n"value4":false,\n"value5":-0.8363667190793456\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":7,\n"value3":9,\n"value4":false,\n"value5":-0.18570450336414635\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":7,\n"value3":10,\n"value4":false,\n"value5":-0.9900463485351121\n}\n],\n"value3":5,\n"value4":2,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0.0,\n"value9":false,\n"value10":16384,\n"value11":{\n"value0":[]\n},\n"value12":7,\n"value13":10\n}\n}\x94bub\x8c\x07fitness\x94G?\xe3\'\x1c\x0e\xc9`\x8aub.'
+
+
+if __name__ == "__main__":
+    setup_logging()
+
+    individual: Individual = pickle.loads(PICKLED_GENOTYPE)
+
+    logging.info(f"Fitness from pickle: {individual.fitness}")
+
+    evaluator = Evaluator(
+        headless=False,
+        num_simulators=1,
+    )
+    fitness = evaluator.evaluate([individual.genotype])[0]
+    logging.info(f"Rerun fitness: {fitness}")
\ No newline at end of file
diff --git a/prototype/tools.py b/prototype/tools.py
new file mode 100644
index 000000000..b52083b64
--- /dev/null
+++ b/prototype/tools.py
@@ -0,0 +1,18 @@
+from individual import Individual
+
+#contains functionality
+
+def find_best_robot(
+    current_best: Individual | None, population: list[Individual]
+) -> Individual:
+    """
+    Return the best robot between the population and the current best individual.
+
+    :param current_best: The current best individual.
+    :param population: The population.
+    :returns: The best individual.
+    """
+    return max(
+        population if current_best is None else [current_best] + population,
+        key=lambda x: x.fitness,
+    )
\ No newline at end of file

From 458343024af1fd7b925c39baceda5b95ead566df Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Fri, 14 Mar 2025 19:18:16 +0100
Subject: [PATCH 4/7] added evaluator with new x_displacement fitness function

---
 prototype/evaluator.py | 93 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)
 create mode 100644 prototype/evaluator.py

diff --git a/prototype/evaluator.py b/prototype/evaluator.py
new file mode 100644
index 000000000..0566a022c
--- /dev/null
+++ b/prototype/evaluator.py
@@ -0,0 +1,93 @@
+"""Evaluator class."""
+
+from genotype import Genotype
+
+from revolve2.experimentation.evolution.abstract_elements import Evaluator as Eval
+from revolve2.modular_robot_simulation import (
+    ModularRobotScene,
+    Terrain,
+    simulate_scenes,
+)
+from revolve2.simulators.mujoco_simulator import LocalSimulator
+from revolve2.standards import terrains
+from revolve2.standards.simulation_parameters import make_standard_batch_parameters
+import math
+from revolve2.modular_robot_simulation import ModularRobotSimulationState
+
+
+def x_displacement(
+    begin_state: ModularRobotSimulationState, end_state: ModularRobotSimulationState
+) -> float:
+    """
+    Calculate the distance traveled on the x-plane by a single modular robot.
+
+    :param begin_state: Begin state of the robot.
+    :param end_state: End state of the robot.
+    :returns: The calculated fitness.
+    """
+    begin_position = begin_state.get_pose().position
+    end_position = end_state.get_pose().position
+    return math.sqrt(
+        (begin_position.x - end_position.x) ** 2
+    )
+
+
+class Evaluator(Eval):
+    """Provides evaluation of robots."""
+
+    _simulator: LocalSimulator
+    _terrain: Terrain
+
+    def __init__(
+        self,
+        headless: bool,
+        num_simulators: int,
+    ) -> None:
+        """
+        Initialize this object.
+
+        :param headless: `headless` parameter for the physics simulator.
+        :param num_simulators: `num_simulators` parameter for the physics simulator.
+        """
+        self._simulator = LocalSimulator(
+            headless=headless, num_simulators=num_simulators
+        )
+        self._terrain = terrains.flat()
+
+    def evaluate(
+        self,
+        population: list[Genotype],
+    ) -> list[float]:
+        """
+        Evaluate multiple robots.
+
+        Fitness is the distance traveled on the xy plane.
+
+        :param population: The robots to simulate.
+        :returns: Fitnesses of the robots.
+        """
+        robots = [genotype.develop() for genotype in population]
+        # Create the scenes.
+        scenes = []
+        for robot in robots:
+            scene = ModularRobotScene(terrain=self._terrain)
+            scene.add_robot(robot)
+            scenes.append(scene)
+
+        # Simulate all scenes.
+        scene_states = simulate_scenes(
+            simulator=self._simulator,
+            batch_parameters=make_standard_batch_parameters(),
+            scenes=scenes,
+        )
+
+        # Calculate the x displacement (only one direction)
+        x_displacements = [
+            x_displacement(
+                states[0].get_modular_robot_simulation_state(robot),
+                states[-1].get_modular_robot_simulation_state(robot),
+            )
+            for robot, states in zip(robots, scene_states)
+        ]
+
+        return x_displacements

From e0bbb38708443d6c5791db616b2d89d301cf8cb1 Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Fri, 14 Mar 2025 19:18:36 +0100
Subject: [PATCH 5/7] fixed typos

---
 .../experimentation/optimization/ea/selection/_tournament.py  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/experimentation/revolve2/experimentation/optimization/ea/selection/_tournament.py b/experimentation/revolve2/experimentation/optimization/ea/selection/_tournament.py
index 42750461f..d842d48a0 100644
--- a/experimentation/revolve2/experimentation/optimization/ea/selection/_tournament.py
+++ b/experimentation/revolve2/experimentation/optimization/ea/selection/_tournament.py
@@ -10,9 +10,9 @@ def tournament(rng: np.random.Generator, fitnesses: list[Fitness], k: int) -> in
     Perform tournament selection and return the index of the best individual.
 
     :param rng: Random number generator.
-    :param fitnesses: List of finesses of individuals that joint the tournamente.
+    :param fitnesses: List of finesses of individuals that joint the tournament.
     :param k: Amount of individuals to participate in tournament.
-    :returns: The index of te individual that won the tournament.
+    :returns: The index of the individual that won the tournament.
     """
     assert len(fitnesses) >= k
 

From aebba4873c213aec57c606b0adf6fbcf7d4e9b36 Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Fri, 14 Mar 2025 19:22:02 +0100
Subject: [PATCH 6/7] fixed typo and corrected return type annotation to int
 for indices (was float previously)

---
 .../optimization/ea/selection/_multiple_unique.py             | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/experimentation/revolve2/experimentation/optimization/ea/selection/_multiple_unique.py b/experimentation/revolve2/experimentation/optimization/ea/selection/_multiple_unique.py
index 95b19074b..4af074d4a 100644
--- a/experimentation/revolve2/experimentation/optimization/ea/selection/_multiple_unique.py
+++ b/experimentation/revolve2/experimentation/optimization/ea/selection/_multiple_unique.py
@@ -12,11 +12,11 @@ def multiple_unique(
     population: list[TIndividual],
     fitnesses: list[TFitness],
     selection_function: Callable[[list[TIndividual], list[TFitness]], int],
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.int_]:
     """
     Select multiple distinct individuals from a population using the provided selection function.
 
-    :param selection_size: Amount of of individuals to select.
+    :param selection_size: Amount of individuals to select.
     :param population: List of individuals to select from.
     :param fitnesses: Fitnesses of the population.
     :param selection_function: Function that select a single individual from a population. ([TIndividual], [TFitness]) -> index.

From 0b1a395cebd17d36b79d7248bbc2f303af2d96d3 Mon Sep 17 00:00:00 2001
From: DanyPaisDaSilva <danypaisdasilva@gmail.com>
Date: Fri, 14 Mar 2025 19:49:38 +0100
Subject: [PATCH 7/7] mostly changed some text

---
 prototype/evaluator.py |  2 +-
 prototype/main.py      | 49 +++++++++++++++++++-----------------------
 prototype/rerun.py     |  3 +--
 prototype/tools.py     |  2 +-
 4 files changed, 25 insertions(+), 31 deletions(-)

diff --git a/prototype/evaluator.py b/prototype/evaluator.py
index 0566a022c..58859e28c 100644
--- a/prototype/evaluator.py
+++ b/prototype/evaluator.py
@@ -61,7 +61,7 @@ def evaluate(
         """
         Evaluate multiple robots.
 
-        Fitness is the distance traveled on the xy plane.
+        Fitness is the distance traveled on the x plane.
 
         :param population: The robots to simulate.
         :returns: Fitnesses of the robots.
diff --git a/prototype/main.py b/prototype/main.py
index 1e9094c51..e903a29bb 100644
--- a/prototype/main.py
+++ b/prototype/main.py
@@ -1,6 +1,5 @@
 import logging
 import pickle
-from tqdm import tqdm
 
 import config
 import multineat
@@ -16,22 +15,19 @@
 
 
 def run() -> None:
-    # Set up logging.
-    setup_logging(file_name="log.txt")
 
+        #########
+        # SETUP #
+        #########
+
+    # initialize logging and rng
+    setup_logging(file_name="log.txt")
     rng = make_rng_time_seed()
 
     # initialize CPPN innovation databases (brain & body)
     innov_db_body = multineat.InnovationDatabase()
     innov_db_brain = multineat.InnovationDatabase()
 
-    """
-    - evaluator: Allows us to evaluate a population of modular robots.
-    - parent_selector: Allows us to select parents from a population of modular robots.
-    - survivor_selector: Allows us to select survivors from a population.
-    - crossover_reproducer: Allows us to generate offspring from parents.
-    - modular_robot_evolution: The evolutionary process as a object that can be iterated.
-    """
     # initialize evolutionary process components
     evaluator = Evaluator(headless=True, num_simulators=config.NUM_SIMULATORS)
     parent_selector = ParentSelector(offspring_size=config.OFFSPRING_SIZE, rng=rng)
@@ -40,14 +36,15 @@ def run() -> None:
         rng=rng, innov_db_body=innov_db_body, innov_db_brain=innov_db_brain
     )
 
-    modular_robot_evolution = ModularRobotEvolution(
+    # initialize ea
+    evolver = ModularRobotEvolution(
         parent_selection=parent_selector,
         survivor_selection=survivor_selector,
         evaluator=evaluator,
         reproducer=crossover_reproducer,
     )
 
-    # Create an initial population as we cant start from nothing.
+    # generate initial genotypes
     logging.info("Generating initial population.")
     initial_genotypes = [
         Genotype.random(
@@ -58,38 +55,36 @@ def run() -> None:
         for _ in range(config.POPULATION_SIZE)
     ]
 
-    # Evaluate the initial population.
+    # evaluate the initial genotypes
     logging.info("Evaluating initial population.")
     initial_fitnesses = evaluator.evaluate(initial_genotypes)
 
-    # Create a population of individuals, combining genotype with fitness.
+    # create population of individuals with fitness.
     population = [
         Individual(genotype, fitness)
         for genotype, fitness in zip(initial_genotypes, initial_fitnesses, strict=True)
     ]
 
-    # Save the best robot
+    # save best robot
     best_robot = find_best_robot(None, population)
 
-    # Set the current generation to 0.
+    # set the current generation to 0
     generation_index = 0
 
-    # Start the actual optimization process.
+
+        ###########################
+        # START EVOLUTIONARY LOOP #
+        ###########################
+
+    # start optimization process.
     logging.info("Start optimization process.")
     while generation_index < config.NUM_GENERATIONS:
         logging.info(f"Generation {generation_index + 1} / {config.NUM_GENERATIONS}.")
 
-        """
-        In contrast to the previous example we do not explicitly stat the order of operations here, but let the ModularRobotEvolution object do the scheduling.
-        This does not give a performance boost, but is more readable and less prone to errors due to mixing up the order.
-
-        Not that you are not restricted to the classical ModularRobotEvolution object, since you can adjust the step function as you want.
-        """
-        population = modular_robot_evolution.step(
-            population
-        )  # Step the evolution forward.
+        # step the evolution forward
+        population = evolver.step(population)
 
-        # Find the new best robot
+        # find the new best robot
         best_robot = find_best_robot(best_robot, population)
 
         logging.info(f"Best robot until now: {best_robot.fitness}")
diff --git a/prototype/rerun.py b/prototype/rerun.py
index e1858e26e..4a151d58e 100644
--- a/prototype/rerun.py
+++ b/prototype/rerun.py
@@ -10,14 +10,13 @@
 
 # This is a pickled genotype we optimized.
 # You can copy your own parameters from the optimization output log.
-PICKLED_GENOTYPE = 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+PICKLED_GENOTYPE = b'\x80\x04\x95\x88\x13\x00\x00\x00\x00\x00\x00\x8c\nindividual\x94\x8c\nIndividual\x94\x93\x94)\x81\x94}\x94(\x8c\x08genotype\x94h\x05\x8c\x08Genotype\x94\x93\x94)\x81\x94}\x94(\x8c\x05brain\x94\x8cGrevolve2.standards.genotypes.cppnwin._multineat_genotype_pickle_wrapper\x94\x8c\x1eMultineatGenotypePickleWrapper\x94\x93\x94)\x81\x94X\xf4\x08\x00\x00{\n"value0":{\n"value0":0,\n"value1":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":2,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":8,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":9,\n"value10":1.0\n}\n],\n"value2":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":8,\n"value3":1,\n"value4":false,\n"value5":-0.06794864201394263\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":8,\n"value3":2,\n"value4":false,\n"value5":0.17108417988846415\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":8,\n"value3":3,\n"value4":false,\n"value5":0.24011150499138546\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":8,\n"value3":4,\n"value4":false,\n"value5":-0.16464075583241637\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":8,\n"value3":5,\n"value4":false,\n"value5":0.22977633480239496\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":8,\n"value3":6,\n"value4":false,\n"value5":0.3248419928837503\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":8,\n"value3":7,\n"value4":false,\n"value5":-0.7515674507248752\n}\n],\n"value3":7,\n"value4":1,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0.0,\n"value9":false,\n"value10":16384,\n"value11":{\n"value0":[]\n},\n"value12":8,\n"value13":7\n}\n}\x94b\x8c\x04body\x94h\r)\x81\x94X\xa8\t\x00\x00{\n"value0":{\n"value0":0,\n"value1":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":1,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":2,\n"value3":0.0,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":1,\n"value10":0.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":6,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":10,\n"value10":1.0\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":7,\n"value2":4,\n"value3":3.025,\n"value4":0.0,\n"value5":0.0,\n"value6":0.0,\n"value7":0,\n"value8":0,\n"value9":10,\n"value10":1.0\n}\n],\n"value2":[\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":6,\n"value3":1,\n"value4":false,\n"value5":0.7574132188798952\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":6,\n"value3":2,\n"value4":false,\n"value5":0.6307925509290383\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":3,\n"value2":6,\n"value3":3,\n"value4":false,\n"value5":0.01637625737534451\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":4,\n"value2":6,\n"value3":4,\n"value4":false,\n"value5":-0.37919500097307026\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":5,\n"value2":6,\n"value3":5,\n"value4":false,\n"value5":-0.6202562022299705\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":1,\n"value2":7,\n"value3":6,\n"value4":false,\n"value5":0.26930546284060899\n},\n{\n"value0":{\n"value0":[]\n},\n"value1":2,\n"value2":7,\n"value3":7,\n"val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
 
 if __name__ == "__main__":
     setup_logging()
 
     individual: Individual = pickle.loads(PICKLED_GENOTYPE)
-
     logging.info(f"Fitness from pickle: {individual.fitness}")
 
     evaluator = Evaluator(
diff --git a/prototype/tools.py b/prototype/tools.py
index b52083b64..89688e666 100644
--- a/prototype/tools.py
+++ b/prototype/tools.py
@@ -1,6 +1,6 @@
 from individual import Individual
 
-#contains functionality
+# contains functionality
 
 def find_best_robot(
     current_best: Individual | None, population: list[Individual]