/
coop_evol.py
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/
coop_evol.py
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# This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""This example contains the evolving test from *Potter, M. and De Jong, K.,
2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted
Subcomponents.* section 4.2.4. The number of species is evolved by adding and
removing species as stagnation occurs.
"""
import random
try:
import matplotlib.pyplot as plt
plt.figure()
except:
plt = False
import numpy
from deap import algorithms
from deap import tools
import coop_base
IND_SIZE = coop_base.IND_SIZE
SPECIES_SIZE = coop_base.SPECIES_SIZE
NUM_SPECIES = 1
TARGET_SIZE = 30
IMPROVMENT_TRESHOLD = 0.5
IMPROVMENT_LENGTH = 5
EXTINCTION_TRESHOLD = 5.0
noise = "*##*###*###*****##*##****#*##*###*#****##******##*#**#*#**######"
schematas = ("1##1###1###11111##1##1111#1##1###1#1111##111111##1#11#1#11######",
"1##1###1###11111##1##1000#0##0###0#0000##000000##0#00#0#00######",
"0##0###0###00000##0##0000#0##0###0#0000##001111##1#11#1#11######")
toolbox = coop_base.toolbox
toolbox.register("evaluateContribution", coop_base.matchSetContribution)
def main(extended=True, verbose=True):
target_set = []
species = []
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = "gen", "species", "evals", "std", "min", "avg", "max"
ngen = 300
g = 0
for i in range(len(schematas)):
size = int(TARGET_SIZE/len(schematas))
target_set.extend(toolbox.target_set(schematas[i], size))
species = [toolbox.species() for _ in range(NUM_SPECIES)]
species_index = list(range(NUM_SPECIES))
last_index_added = species_index[-1]
# Init with random a representative for each species
representatives = [random.choice(species[i]) for i in range(NUM_SPECIES)]
best_fitness_history = [None] * IMPROVMENT_LENGTH
if plt and extended:
contribs = [[]]
stag_gen = []
collab = []
while g < ngen:
# Initialize a container for the next generation representatives
next_repr = [None] * len(species)
for (i, s), j in zip(enumerate(species), species_index):
# Vary the species individuals
s = algorithms.varAnd(s, toolbox, 0.6, 1.0)
# Get the representatives excluding the current species
r = representatives[:i] + representatives[i+1:]
for ind in s:
# Evaluate and set the individual fitness
ind.fitness.values = toolbox.evaluate([ind] + r, target_set)
record = stats.compile(s)
logbook.record(gen=g, species=j, evals=len(s), **record)
if verbose:
print(logbook.stream)
# Select the individuals
species[i] = toolbox.select(s, len(s)) # Tournament selection
next_repr[i] = toolbox.get_best(s)[0] # Best selection
if plt and extended:
# Book keeping of the collaborative fitness
collab.append(next_repr[i].fitness.values[0])
g += 1
representatives = next_repr
# Keep representatives fitness for stagnation detection
best_fitness_history.pop(0)
best_fitness_history.append(representatives[0].fitness.values[0])
try:
diff = best_fitness_history[-1] - best_fitness_history[0]
except TypeError:
diff = float("inf")
if plt and extended:
for (i, rep), j in zip(enumerate(representatives), species_index):
contribs[j].append((toolbox.evaluateContribution(representatives,
target_set, i)[0], g-1))
if diff < IMPROVMENT_TRESHOLD:
if len(species) > 1:
contributions = []
for i in range(len(species)):
contributions.append(toolbox.evaluateContribution(representatives, target_set, i)[0])
for i in reversed(range(len(species))):
if contributions[i] < EXTINCTION_TRESHOLD:
species.pop(i)
species_index.pop(i)
representatives.pop(i)
last_index_added += 1
best_fitness_history = [None] * IMPROVMENT_LENGTH
species.append(toolbox.species())
species_index.append(last_index_added)
representatives.append(random.choice(species[-1]))
if extended and plt:
stag_gen.append(g-1)
contribs.append([])
if extended:
for r in representatives:
# print final representatives without noise
print("".join(str(x) for x, y in zip(r, noise) if y == "*"))
if extended and plt: # Plotting of the evolution
line1, = plt.plot(collab, "--", color="k")
for con in contribs:
try:
con, g = zip(*con)
line2, = plt.plot(g, con, "-", color="k")
except ValueError:
pass
axis = plt.axis("tight")
for s in stag_gen:
plt.plot([s, s], [0, axis[-1]], "--", color="k")
plt.legend((line1, line2), ("Collaboration", "Contribution"), loc="center right")
plt.xlabel("Generations")
plt.ylabel("Fitness")
plt.show()
if __name__ == "__main__":
main()