def mating(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location,
Populations[pop_name].agents)
for x in range(len(group) // 2):
parents = []
for i in range(2):
parents.append(random.choice(Populations[pop_name].agents))
while parents[i] not in group:
parents[i] = random.choice(
Populations[pop_name].agents)
Populations[pop_name].agents.remove(parents[i])
crossover_pt = random.randint(
0, len(parents[0].genome[0].sequence))
(new_chromo1,
new_chromo2) = dose.genetic.crossover(parents[0].genome[0],
parents[1].genome[0],
crossover_pt)
children = [
dose.genetic.Organism([new_chromo1],
parameters["mutation_type"],
parameters["additional_mutation"]),
dose.genetic.Organism([new_chromo2],
parameters["mutation_type"],
parameters["additional_mutation"])
]
for child in children:
child.status['parents'] = [
parents[0].status['identity'],
parents[1].status['identity']
]
child.status['location'] = location
child.generate_name()
child.status['deme'] = pop_name
Populations[pop_name].agents.append(child)
def mating(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location, Populations[pop_name].agents)
for x in range(len(group)//2):
parents = []
for i in range(2):
parents.append(random.choice(Populations[pop_name].agents))
while parents[i] not in group:
parents[i] = random.choice(Populations[pop_name].agents)
Populations[pop_name].agents.remove(parents[i])
crossover_pt = random.randint(0, len(parents[0].genome[0].sequence))
(new_chromo1, new_chromo2) = dose.genetic.crossover(parents[0].genome[0],
parents[1].genome[0],
crossover_pt)
children = [dose.genetic.Organism([new_chromo1],
parameters["mutation_type"],
parameters["additional_mutation"]),
dose.genetic.Organism([new_chromo2],
parameters["mutation_type"],
parameters["additional_mutation"])]
for child in children:
child.status['parents'] = [parents[0].status['identity'],
parents[1].status['identity']]
child.status['location'] = location
child.generate_name()
child.status['deme'] = pop_name
Populations[pop_name].agents.append(child)
def mating(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location, Populations[pop_name].agents)
for x in range(parameters["eco_cell_capacity"] - len(group)):
parents = []
alpha_organism = group[0]
for organism in group:
if abs(parameters["goal"] - organism.status['fitness']) < \
abs(parameters["goal"] - alpha_organism.status['fitness']):
alpha_organism = organism
parents.append(alpha_organism)
parents.append(random.choice(group))
crossover_pt = random.randint(0, len(parents[0].genome[0].sequence))
(new_chromo1, new_chromo2) = dose.genetic.crossover(parents[0].genome[0],
parents[1].genome[0],
crossover_pt)
child = dose.genetic.Organism([new_chromo1],
parameters["mutation_type"],
parameters["additional_mutation"])
child.status['parents'] = [parents[0].status['identity'],
parents[1].status['identity']]
child.status['location'] = location
child.generate_name()
child.status['deme'] = pop_name
Populations[pop_name].agents.append(child)
def mating(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location,
Populations[pop_name].agents)
for x in range(parameters["eco_cell_capacity"] - len(group)):
parents = []
alpha_organism = group[0]
for organism in group:
if abs(parameters["goal"] - organism.status['fitness']) < \
abs(parameters["goal"] - alpha_organism.status['fitness']):
alpha_organism = organism
parents.append(alpha_organism)
parents.append(random.choice(group))
crossover_pt = random.randint(
0, len(parents[0].genome[0].sequence))
(new_chromo1,
new_chromo2) = dose.genetic.crossover(parents[0].genome[0],
parents[1].genome[0],
crossover_pt)
child = dose.genetic.Organism(
[new_chromo1], parameters["mutation_type"],
parameters["additional_mutation"])
child.status['parents'] = [
parents[0].status['identity'],
parents[1].status['identity']
]
child.status['location'] = location
child.generate_name()
child.status['deme'] = pop_name
Populations[pop_name].agents.append(child)
def postpopulation_control(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location, Populations[pop_name].agents)
average_fitness = sum([organism.status["fitness"] for organism in group])/len(group)
if average_fitness != parameters["goal"]:
omega_organism_fitness = 0
for organism in group:
if abs(parameters["goal"] - organism.status['fitness']) > \
abs(parameters["goal"] - omega_organism_fitness):
omega_organism_fitness = int(organism.status['fitness'])
for organism in group:
if organism.status['fitness'] in range(omega_organism_fitness - 2,
omega_organism_fitness + 2):
Populations[pop_name].agents.remove(organism)
def organism_movement(self, Populations, pop_name, World):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location, Populations[pop_name].agents)
adj_cells = dose.simulation_calls.adjacent_cells(parameters, location)
for i in range(int(round((len(group) * 0.1)))):
(x,y,z) = dose.simulation_calls.coordinates(location)
World.ecosystem[x][y][z]['organisms'] -= 1
immigrant = random.choice(Populations[pop_name].agents)
while immigrant not in group:
immigrant = random.choice(Populations[pop_name].agents)
new_location = random.choice(adj_cells)
immigrant.status['location'] = new_location
(x,y,z) = dose.simulation_calls.coordinates(new_location)
World.ecosystem[x][y][z]['organisms'] += 1
def organism_movement(self, Populations, pop_name, World):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location,
Populations[pop_name].agents)
adj_cells = dose.simulation_calls.adjacent_cells(
parameters, location)
for i in range(int(round((len(group) * 0.1)))):
(x, y, z) = dose.simulation_calls.coordinates(location)
World.ecosystem[x][y][z]['organisms'] -= 1
immigrant = random.choice(Populations[pop_name].agents)
while immigrant not in group:
immigrant = random.choice(Populations[pop_name].agents)
new_location = random.choice(adj_cells)
immigrant.status['location'] = new_location
(x, y, z) = dose.simulation_calls.coordinates(new_location)
World.ecosystem[x][y][z]['organisms'] += 1
def prepopulation_control(self, Populations, pop_name):
for location in parameters["population_locations"][0]:
group = dose.filter_location(location, Populations[pop_name].agents)
average_fitness = sum([organism.status["fitness"] for organism in group])/len(group)
if average_fitness != parameters["goal"]:
alpha_organism_fitness = 0
for organism in group:
if abs(parameters["goal"] - organism.status['fitness']) < \
abs(parameters["goal"] - alpha_organism_fitness):
alpha_organism_fitness = int(organism.status['fitness'])
for organism in group:
if organism.status['fitness'] not in range(alpha_organism_fitness - 2,
alpha_organism_fitness + 2):
Populations[pop_name].agents.remove(organism)
else:
if organism.status['fitness'] != parameters["goal"]:
Populations[pop_name].agents.remove(organism)