def evaluate_individ_obj_function(genome, generation):
"""
The function to evaluate individual objective function
Arguments:
genome: The objective function genome
generation: The current generation of evolution
Returns:
The NoveltyItem created using evaluation results.
"""
# create NoveltyItem for genome and store it into map
genome_id = genome.GetID()
n_item = archive.NoveltyItem(generation=generation, genomeId=genome_id)
# run the simulation
multi_net = NEAT.NeuralNetwork()
genome.BuildPhenotype(multi_net)
depth = 2
try:
genome.CalculateDepth()
depth = genome.GetDepth()
except:
pass
obj_net = ANN(multi_net, depth=depth)
# set inputs and get ouputs ([a, b])
output = obj_net.activate([0.5])
# store coefficients
n_item.data.append(output[0])
n_item.data.append(output[1])
return n_item
def eval_individual(genome_id, genome, genomes, n_items_map, config):
"""
Evaluates the individual represented by genome.
Arguments:
genome_id: The ID of genome.
genome: The genome to evaluate.
genomes: The genomes population for current generation.
n_items_map: The map to hold novelty items for current generation.
config: The NEAT configuration holder.
Return:
The True if successful solver found.
"""
# create NoveltyItem for genome and store it into map
n_item = archive.NoveltyItem(generation=trial_sim.population.generation,
genomeId=genome_id)
n_items_map[genome_id] = n_item
# run the simulation
maze_env = copy.deepcopy(trial_sim.orig_maze_environment)
control_net = neat.nn.FeedForwardNetwork.create(genome, config)
goal_fitness = maze.maze_simulation_evaluate(env=maze_env,
net=control_net,
time_steps=SOLVER_TIME_STEPS,
n_item=n_item,
mcns=MCNS)
if goal_fitness == -1:
# The individual doesn't meet the minimal fitness criterion
print("Individ with ID: %d marked for extiction, MCNS: %f" %
(genome_id, MCNS))
return False
# Store simulation results into the agent record
record = agent.AgentRecord(generation=trial_sim.population.generation,
agent_id=genome_id)
record.fitness = goal_fitness
record.x = maze_env.agent.location.x
record.y = maze_env.agent.location.y
record.hit_exit = maze_env.exit_found
record.species_id = trial_sim.population.species \
.get_species_id(genome_id)
record.species_age = record.generation - \
trial_sim.population.species.get_species(genome_id).created
# add record to the store
trial_sim.record_store.add_record(record)
# Evaluate the novelty of a genome and add the novelty item to the archive of Novelty items if appropriate
if not maze_env.exit_found:
# evaluate genome novelty and add it to the archive if appropriate
record.novelty = trial_sim.archive \
.evaluate_individual_novelty(genome=genome, genomes=genomes,
n_items_map=n_items_map)
# update fittest organisms list
trial_sim.archive.update_fittest_with_genome(genome=genome,
n_items_map=n_items_map)
return maze_env.exit_found
def eval_individual(genome_id, genome, genomes, n_items_map, generation):
"""
Evaluates the individual represented by genome.
Arguments:
genome_id: The ID of genome.
genome: The genome to evaluate.
genomes: The genomes population for current generation.
n_items_map: The map to hold novelty items for current generation.
generation: The current generation.
Return:
The True if successful solver found.
"""
# create NoveltyItem for genome and store it into map
n_item = archive.NoveltyItem(generation=generation, genomeId=genome_id)
n_items_map[genome_id] = n_item
# run the simulation
maze_env = copy.deepcopy(trial_sim.orig_maze_environment)
multi_net = NEAT.NeuralNetwork()
genome.BuildPhenotype(multi_net)
control_net = ANN(multi_net)
goal_fitness = maze.maze_simulation_evaluate(
env=maze_env,
net=control_net,
time_steps=SOLVER_TIME_STEPS,
n_item=n_item)
# Store simulation results into the agent record
record = agent.AgenRecord(generation=generation, agent_id=genome_id)
record.fitness = goal_fitness
record.x = maze_env.agent.location.x
record.y = maze_env.agent.location.y
record.hit_exit = maze_env.exit_found
#record.species_id = trial_sim.population.species.get_species_id(genome_id)
#record.species_age = record.generation - trial_sim.population.species.get_species(genome_id).created
# add record to the store
trial_sim.record_store.add_record(record)
# Evaluate the novelty of a genome and add the novelty item to the archive of Novelty items if appropriate
if not maze_env.exit_found:
# evaluate genome novelty and add it to the archive if appropriate
record.novelty = trial_sim.archive.evaluate_individual_novelty(genome=Genome(genome),
genomes=genomes, n_items_map=n_items_map)
# update fittest organisms list
trial_sim.archive.update_fittest_with_genome(genome=Genome(genome), n_items_map=n_items_map)
return (maze_env.exit_found, goal_fitness)
def evaluate_individual_solution(genome, generation, robot):
"""
The function to evaluate individual solution against maze environment.
Arguments:
genome: The genome to evaluate.
generation: The current generation.
robot: The object encapsulating the robots population
Return:
The tuple specifying if solution was found and the distance from maze exit of final robot position.
"""
# create NoveltyItem for genome and store it into map
genome_id = genome.GetID()
n_item = archive.NoveltyItem(generation=generation, genomeId=genome_id)
# run the simulation
maze_env = copy.deepcopy(robot.orig_maze_environment)
multi_net = NEAT.NeuralNetwork()
genome.BuildPhenotype(multi_net)
depth = 8
try:
genome.CalculateDepth()
depth = genome.GetDepth()
except:
pass
control_net = ANN(multi_net, depth=depth)
distance = maze.maze_simulation_evaluate(env=maze_env,
net=control_net,
time_steps=SOLVER_TIME_STEPS,
n_item=n_item)
# Store simulation results into the agent record
record = agent.AgenRecord(generation=generation, agent_id=genome_id)
record.distance = distance
record.x = maze_env.agent.location.x
record.y = maze_env.agent.location.y
record.hit_exit = maze_env.exit_found
record.species_id = robot.get_species_id(genome)
robot.record_store.add_record(record)
return (maze_env.exit_found, distance, n_item)