def create_variants(self, n, desc, category, constructor):
def assign_2nd_alg(archipelago, algo):
if category == 'rings':
for island in archipelago.topology.every_other_island():
island.algorithm = algo
elif hasattr(archipelago.topology, 'endpoints'):
for island in archipelago.topology.endpoints:
island.algorithm = algo
elif isinstance(archipelago.topology, FullyConnectedTopology):
for island in islice(archipelago.topology.islands, None, None, 2):
island.algorithm = algo
return archipelago
def assign_algs(archipelago, algos):
'''
Evenly partitions and assigns algorithms to islands.
'''
for island,algo in zip(archipelago.topology.islands, cycle(algos)):
island.algorithm = algo
g = self.generations
self.new_topology(
desc='{}, de'.format(desc),
category=category,
algorithms=['de'],
archipelago=Archipelago(constructor(de(gen=g),n)))
self.new_topology(
desc='{}, de1220'.format(desc),
category=category,
algorithms=['de1220'],
archipelago=Archipelago(constructor(de1220(gen=g),n)))
self.new_topology(
desc='{}, sade'.format(desc),
category=category,
algorithms=['sade'],
archipelago=Archipelago(constructor(sade(gen=g),n)))
self.new_topology(
desc='{}, bee_colony'.format(desc),
category=category,
algorithms=['bee_colony'],
archipelago=Archipelago(constructor(bee_colony(gen=g),n)))
# de + nelder mead combo
self.new_topology(
desc='{}, de+nelder mead'.format(desc),
category=category,
algorithms=['de','neldermead'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), self.make_nelder_mead()))
# de + praxis combo
self.new_topology(
desc='{}, de+praxis'.format(desc),
category=category,
algorithms=['de','praxis'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), self.make_praxis()))
# de + sade combo
self.new_topology(
desc='{}, de+sade'.format(desc),
category=category,
algorithms=['de','sade'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), sade(gen=g)))
def user_defined_problem():
prob = pg.problem(sphere_function(3))
print(prob)
algo = pg.algorithm(pg.bee_colony(gen = 200, limit = 20))
pop = pg.population(prob, 10)
pop = algo.evolve(pop)
print(pop.champion_f)
print(pop)
def user_defined_problem():
prob = pg.problem(sphere_function(3))
print(prob)
algo = pg.algorithm(pg.bee_colony(gen=200, limit=20))
pop = pg.population(prob, 10)
pop = algo.evolve(pop)
print(pop.champion_f)
print(pop)
def test_pagmo(joint_likelihood_bn090217206_nai):
pagmo = GlobalMinimization("PAGMO")
minuit = LocalMinimization("minuit")
algo = pygmo.algorithm(pygmo.bee_colony(gen=100))
pagmo.setup(islands=4, population_size=20, evolution_cycles=1, second_minimization=minuit, algorithm=algo)
do_analysis(joint_likelihood_bn090217206_nai, pagmo)
archi.wait_check()
print(archi)
import pygmo as pg
# The user-defined problem
udp = pg.schwefel(dim=20)
# The pygmo problem
prob = pg.problem(udp)
# For a number of generation based algorithms we can use a similar script to run and average over 25 runs.
udas = [
pg.sade(gen=500),
pg.de(gen=500),
pg.de1220(gen=500),
pg.pso(gen=500),
pg.bee_colony(gen=250, limit=20)
]
for uda in udas:
logs = []
for i in range(25):
algo = pg.algorithm(uda)
algo.set_verbosity(1) # regulates both screen and log verbosity
pop = pg.population(prob, 20)
pop = algo.evolve(pop)
logs.append(algo.extract(type(uda)).get_log())
logs = np.array(logs)
avg_log = np.average(logs, 0)
plt.plot(avg_log[:, 1],
avg_log[:, 2] - 418.9829 * 20,
label=algo.get_name())
def chi_optimize(method='pso',
parallel=False,
N_ind=100,
N_gen=30,
iset=0,
show_results=False,
dir='../data/',
file_name='chi_fit'):
# Load experimental data
N_set = 2
ca_data = svu.loaddata('../data/herzog_data.pkl')[0]
po_data = svu.loaddata('../data/fit_po.pkl')[0]
lr_data = svu.loaddata('../data/fit_lra.pkl')[0]
# Get boundary dictionary
bounds_dict = models.model_bounds(model='chi')
# Save effective bounds and scaling
bounds, scaling = bounds_for_exploration(bounds_dict, normalized=False)
# Prepare model parameters
c0 = 5.0
# c0 = 15.0
params = np.asarray(np.r_[po_data[[0, 1, 0, 2, 3]], lr_data[[0, 1]], c0,
iset])
args = (params, ca_data)
#-----------------------------------------------------------------------------------------------------------
# Effective evolution
#-----------------------------------------------------------------------------------------------------------
# Size parameters
# N_var = problem_dimension(model='lra')
N_individuals = N_ind
N_var = problem_dimension(model='chi')
N_generations = N_gen * N_var
# Tolerances
FTOL = 1e-6
XTOL = 1e-6
# prob = pg.problem(fit_problem('lra',args))
prob = pg.problem(fit_problem('chi', args))
# Optimization
# NOTE: We keep each algorithm self contained here because different setting of parameters for evolution are
# needed depending on the chose algorithm
if method == 'pso':
algo = pg.algorithm(
pg.pso(gen=N_generations, variant=5, neighb_type=4, max_vel=0.8))
elif method == 'bee':
algo = pg.algorithm(pg.bee_colony(gen=N_generations, limit=2))
elif method == 'de':
algo = pg.algorithm(pg.de(gen=N_generations, ftol=FTOL, tol=XTOL))
# Single-node optimation to run on local machine / laptop
# N_individuals = 100
# N_generations = 40 * N_var
# verbosity = 20
if not parallel:
verbosity = 20
algo.set_verbosity(verbosity)
pop = pg.population(prob, size=N_individuals)
pop = algo.evolve(pop)
best_fitness = pop.get_f()[pop.best_idx()]
print(best_fitness)
x_rescaled = rescale_vector(pop.champion_x,
model='chi',
normalized=True,
bounds=bounds,
scaling=scaling)
# Show results of fit
if show_results:
astro = models.Astrocyte(
model='chi',
d1=po_data[0],
d2=po_data[1],
d3=po_data[0],
d5=po_data[2],
a2=po_data[3],
c0=c0,
c1=0.5,
rl=0.1,
Ker=0.1,
rc=lr_data[0],
ver=lr_data[1],
vbeta=x_rescaled[0],
vdelta=x_rescaled[1],
v3k=x_rescaled[2],
r5p=x_rescaled[3],
ICs=np.asarray([x_rescaled[6], x_rescaled[4], x_rescaled[5]]))
options = su.solver_opts(t0=ca_data['time'][iset][0],
tfin=ca_data['time'][iset][-1],
dt=1e-4,
atol=1e-8,
rtol=1e-6,
method="gsl_msadams")
astro.integrate(algparams=options, normalized=True)
ca_trace = ca_data['smoothed'][iset]
plt.plot(ca_data['time'][0], ca_trace, 'k-', astro.sol['ts'],
astro.sol['ca'], 'r-')
plt.show()
else:
# Parallel version to run on cluster
N_evolutions = 100
N_islands = 10
# Initiate optimization
algo = pg.algorithm(
pg.pso(gen=N_generations, variant=5, neighb_type=4, max_vel=0.8))
archi = pg.archipelago(n=N_islands,
algo=algo,
prob=prob,
pop_size=N_individuals)
archi.evolve(N_evolutions)
archi.wait()
imin = np.argmin(archi.get_champions_f())
x_rescaled = rescale_vector(archi.get_champions_x()[imin],
model='chi',
normalized=True,
bounds=bounds,
scaling=scaling)
print archi.get_champions_f()[imin]
print x_rescaled
svu.savedata([x_rescaled], dir + file_name + '.pkl')
def popen_optimize(method='de-simple'):
# Load experimental data
p_open = svu.loaddata('../data/p_open.pkl')[0]
# Get boundary dictionary
# bounds_dict = models.model_bounds(model='lra')
bounds_dict = models.model_bounds(model='lra2')
# Save effective bounds and scaling
bounds, scaling = bounds_for_exploration(bounds_dict, normalized=False)
args = (p_open['ca'], p_open['ip3'])
#-----------------------------------------------------------------------------------------------------------
# Effective evolution
#-----------------------------------------------------------------------------------------------------------
# Size parameters
# N_var = problem_dimension(model='lra')
N_var = problem_dimension(model='lra2')
N_individuals = 100
N_generations = 500 * N_var
N_evolutions = 50
# Tolerances
FTOL = 1e-3
XTOL = 1e-3
# prob = pg.problem(fit_problem('lra',args))
prob = pg.problem(fit_problem('lra2', args))
# Optimization
# NOTE: We keep each algorithm self contained here because different setting of parameters for evolution are
# needed depending on the chose algorithm
if method == 'de-simple':
##-----------------
# # Single node
##-----------------
# algo = pg.algorithm(pg.de(gen=N_generations,F=0.75,CR=0.9,variant=6,ftol=FTOL,xtol=XTOL))
# algo = pg.algorithm(pg.de(gen=N_generations, ftol=1e-12, tol=1e-6))
# algo = pg.algorithm(pg.pso(gen=N_generations))
algo = pg.algorithm(pg.bee_colony(gen=N_generations))
algo.set_verbosity(100)
pop = pg.population(prob, size=N_individuals)
pop = algo.evolve(pop)
best_fitness = pop.get_f()[pop.best_idx()]
print(best_fitness)
x_rescaled = rescale_vector(pop.champion_x,
model='lra2',
normalized=True,
bounds=bounds,
scaling=scaling)
astro = models.Astrocyte(model='lra2',
d1=x_rescaled[0],
d2=x_rescaled[1],
d5=x_rescaled[2],
a2=x_rescaled[3])
astro.popen(p_open['ca'][0], 1.0)
po_ca = astro.po
astro.popen(0.25, p_open['ip3'][0])
po_ip3 = astro.po
print x_rescaled
svu.savedata([x_rescaled], '../data/fit_po.pkl')
plt.semilogx(p_open['ca'][0], p_open['ca'][1], 'k-', p_open['ca'][0],
po_ca, 'ro', p_open['ip3'][0], p_open['ip3'][1], 'm-',
p_open['ip3'][0], po_ip3, 'bo')
plt.show()
n_grad - 1, str(grad_res[-1, :]), expr_eval(f, grad_res[-1, :])))
#%% Применяем Pygmo с covariance Matrix Evolutionary Strategy (CMA-ES)
class Ackley_function:
def fitness(self, x):
return [expr_eval(f, x)]
def get_bounds(self):
return ([x[0], y[0]], [x[-1], y[-1]])
prob = pg.problem(Ackley_function())
algo = pg.algorithm(pg.bee_colony(gen=gen, seed=42))
algo.set_verbosity(100)
pop = pg.population(prob, n_pop)
pop = algo.evolve(pop)
n_pygmo = pop.get_x().shape[0]
print(pop)
#%% Визуализация для функции двух аргументов через линии уровня
print("Градиентный Спуск: {:f}, Pygmo: {:f}".format(
expr_eval(f, grad_res[-1, :]), pop.champion_f[0]))
n_levels = 15 # число линий уровня
markersize = 5
def __call__(self, function):
scanner_options = {
'sade':
dict(gen=self.gen,
variant=self.variant,
variant_adptv=self.variant_adptv,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
seed=self.seed),
'gaco':
dict(gen=self.gen,
ker=self.ker,
q=self.q,
oracle=self.oracle,
acc=self.acc,
threshold=self.threshold,
n_gen_mark=self.n_gen_mark,
impstop=self.impstop,
evalstop=self.evalstop,
focus=self.focus,
memory=self.memory,
seed=self.seed),
'maco':
dict(gen=self.gen,
ker=self.ker,
q=self.q,
threshold=self.threshold,
n_gen_mark=self.n_gen_mark,
evalstop=self.evalstop,
focus=self.focus,
memory=self.memory,
seed=self.seed),
'gwo':
dict(gen=self.gen, seed=self.seed),
'bee_colony':
dict(gen=self.gen, limit=self.limit, seed=self.seed),
'de':
dict(gen=self.gen,
F=self.F,
CR=self.CR,
variant=self.variant,
ftol=self.ftol,
xtol=self.xtol,
seed=self.seed),
'sea':
dict(gen=self.gen, seed=self.seed),
'sga':
dict(gen=self.gen,
cr=self.cr,
eta_c=self.eta_c,
m=self.m,
param_m=self.param_m,
param_s=self.param_s,
crossover=self.crossover,
mutation=self.mutation,
selection=self.selection,
seed=self.seed),
'de1220':
dict(gen=self.gen,
allowed_variants=self.allowed_variants,
variant_adptv=self.variant_adptv,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
seed=self.seed),
'cmaes':
dict(gen=self.gen,
cc=self.cc,
cs=self.cs,
c1=self.c1,
cmu=self.cmu,
sigma0=self.sigma0,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
force_bounds=self.force_bounds,
seed=self.seed),
'moead':
dict(gen=self.gen,
weight_generation=self.weight_generation,
decomposition=self.decomposition,
neighbours=self.neighbours,
CR=self.CR,
F=self.F,
eta_m=self.eta_m,
realb=self.realb,
limit=self.limit,
preserve_diversity=self.preserve_diversity,
seed=self.seed),
'compass_search':
dict(max_fevals=self.max_fevals,
start_range=self.start_range,
stop_range=self.stop_range,
reduction_coeff=self.reduction_coeff),
'simulated_annealing':
dict(Ts=self.Ts,
Tf=self.Tf,
n_T_adj=self.n_T_adj,
n_range_adj=self.n_range_adj,
bin_size=self.bin_size,
start_range=self.start_range,
seed=self.seed),
'pso':
dict(gen=self.gen,
omega=self.omega,
eta1=self.eta1,
eta2=self.eta2,
max_vel=self.max_vel,
variant=self.variant,
neighb_type=self.neighb_type,
neighb_param=self.neighb_param,
memory=self.memory,
seed=self.seed),
'pso_gen':
dict(gen=self.gen,
omega=self.omega,
eta1=self.eta1,
eta2=self.eta2,
max_vel=self.max_vel,
variant=self.variant,
neighb_type=self.neighb_type,
neighb_param=self.neighb_param,
memory=self.memory,
seed=self.seed),
'nsga2':
dict(gen=self.gen,
cr=self.cr,
eta_c=self.eta_c,
m=self.m,
eta_m=self.eta_m,
seed=self.seed),
'nspso':
dict(gen=self.gen,
omega=self.omega,
c1=self.c1,
c2=self.c2,
chi=self.chi,
v_coeff=self.v_coeff,
leader_selection_range=self.leader_selection_range,
diversity_mechanism=self.diversity_mechanism,
memory=self.memory,
seed=self.seed),
'mbh':
dict(algo=self.algo,
stop=self.stop,
perturb=self.perturb,
seed=self.seed),
'cstrs_self_adaptive':
dict(iters=self.iters, algo=self.algo, seed=self.seed),
'ihs':
dict(gen=self.gen,
phmcr=self.phmcr,
ppar_min=self.ppar_min,
ppar_max=self.ppar_max,
bw_min=self.bw_min,
bw_max=self.bw_max,
seed=self.seed),
'xnes':
dict(gen=self.gen,
eta_mu=self.eta_mu,
eta_sigma=self.eta_sigma,
eta_b=self.eta_b,
sigma0=self.sigma0,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
force_bounds=self.force_bounds,
seed=self.seed)
}
if self.log_data:
xl = []
yl = []
log_data = self.log_data
#
class interf_function:
def __init__(self, dim):
self.dim = dim
def fitness(self, x):
x = np.expand_dims(x, axis=0)
y = function(x)
# x = x[0]
y = y.tolist()
if log_data:
xl.append(x)
yl.append(y)
# print (x, y[0])
return y[0]
if function.is_differentiable():
def gradient(self, x):
x = np.expand_dims(x, axis=0)
g = function(x)
g = g.tolist()
return g[0]
def get_bounds(self):
lb = []
ub = []
bounds = function.get_ranges()
# warning
# check for infinities
for i in range(len(bounds)):
lb.append(bounds[i, 0])
ub.append(bounds[i, 1])
r = (np.array(lb), np.array(ub))
return r
# I need to call pygmo functions directly
prob = pg.problem(interf_function(function))
# print (prob.get_thread_safety())
if self.scanner == "sade":
# I need a dictionary with algorithms and options
algo = pg.algorithm(pg.sade(**scanner_options[self.scanner]))
elif self.scanner == "gaco":
algo = pg.algorithm(pg.gaco(**scanner_options[self.scanner]))
# elif self.scanner == "maco": # is not implemented though in webpage
# looks it is
# algo = pg.algorithm(pg.maco(**scanner_options[self.scanner]))
elif self.scanner == "gwo":
algo = pg.algorithm(pg.gwo(**scanner_options[self.scanner]))
elif self.scanner == "bee_colony":
algo = pg.algorithm(pg.bee_colony(**scanner_options[self.scanner]))
elif self.scanner == "de":
algo = pg.algorithm(pg.de(**scanner_options[self.scanner]))
elif self.scanner == "sea":
algo = pg.algorithm(pg.sea(**scanner_options[self.scanner]))
elif self.scanner == "sga":
algo = pg.algorithm(pg.sga(**scanner_options[self.scanner]))
elif self.scanner == "de1220":
algo = pg.algorithm(pg.de1220(**scanner_options[self.scanner]))
elif self.scanner == "cmaes":
algo = pg.algorithm(pg.cmaes(**scanner_options[self.scanner]))
# elif self.scanner == "moead": #multiobjective algorithm
# algo = pg.algorithm(pg.moead(**scanner_options[self.scanner]))
elif self.scanner == "compass_search":
algo = pg.algorithm(
pg.compass_search(**scanner_options[self.scanner]))
elif self.scanner == 'simulated_annealing':
algo = pg.algorithm(
pg.simulated_annealing(**scanner_options[self.scanner]))
elif self.scanner == 'pso':
algo = pg.algorithm(pg.pso(**scanner_options[self.scanner]))
elif self.scanner == 'pso_gen':
algo = pg.algorithm(pg.pso_gen(**scanner_options[self.scanner]))
# elif self.scanner == 'nsga2': #multiobjective algorithm
# algo = pg.algorithm(pg.nsga2(**scanner_options[self.scanner]))
# elif self.scanner == 'nspso': is not implemented though in webpage
# looks it is
# algo = pg.algorithm(pg.nspso(**scanner_options[self.scanner]))
elif self.scanner == 'mbh':
if scanner_options[self.scanner]['algo'] == 'de':
algo = pg.algorithm(
pg.mbh(pg.algorithm(pg.de(**scanner_options['de']))))
# elif self.scanner == 'ihs': #does not work
# algo = pg.algorithm(ihs(**scanner_options[self.scanner]))
# elif self.scanner == 'xnes': #does not work
# algo = pg.algorithm(xnes(**scanner_options[self.scanner]))
# uda = algo.extract(xnes)
else:
print(
'The ' + self.scanner + ' algorithm is not implemented. The '
'list of algorithms available is', algorithms)
sys.exit()
# add verbosing flag
if self.verbose > 1:
algo.set_verbosity(self.verbose)
pop = pg.population(prob, self.size)
if self.verbose > 9:
print('prob', prob)
opt = algo.evolve(pop)
if self.verbose > 9:
print('algo', algo)
# best_x = np.expand_dims(opt.champion_x, axis=0)
# best_fitness = np.expand_dims(opt.get_f()[opt.best_idx()], axis=0)
best_x = np.expand_dims(opt.champion_x, axis=0)
best_fitness = np.expand_dims(opt.champion_f, axis=0)
if self.verbose > 0:
print('best fit:', best_x, best_fitness)
if self.log_data:
x = np.squeeze(xl, axis=(1, ))
y = np.squeeze(yl, axis=(2, ))
if self.log_data:
return (x, y)
else:
return (best_x, best_fitness)
# !/usr/bin/env python # -*- coding:utf-8 -*- # Author: [email protected] import pygmo as pg class UdpFunction: def __init__(self, dim): self.dim = dim def fitness(self, x): return [sum(x * x)] def get_bounds(self): return ([-1] * self.dim, [1] * self.dim) def get_name(self): return "Udp Function!" algo = pg.algorithm(pg.bee_colony(gen=20, limit=20)) prob = pg.problem(UdpFunction(3)) pop = pg.population(prob, 10) pop = algo.evolve(pop) print(prob) print(pop.champion_f) print(pop.champion_x)
def create_variants(self, n, desc, category, constructor):
def assign_2nd_alg(archipelago, algo):
if category == 'rings':
for island in archipelago.topology.every_other_island():
island.algorithm = algo
elif hasattr(archipelago.topology, 'endpoints'):
for island in archipelago.topology.endpoints:
island.algorithm = algo
elif isinstance(archipelago.topology, FullyConnectedTopology):
for island in islice(archipelago.topology.islands, None, None, 2):
island.algorithm = algo
return archipelago
def assign_algs(archipelago, algos):
'''
Evenly partitions and assigns algorithms to islands.
'''
for island,algo in zip(archipelago.topology.islands, cycle(algos)):
island.algorithm = algo
g = self.generations
self.new_topology(
desc='{}, de'.format(desc),
category=category,
algorithms=['de'],
archipelago=Archipelago(constructor(de(gen=g),n)))
self.new_topology(
desc='{}, de1220'.format(desc),
category=category,
algorithms=['de1220'],
archipelago=Archipelago(constructor(de1220(gen=g),n)))
self.new_topology(
desc='{}, sade'.format(desc),
category=category,
algorithms=['sade'],
archipelago=Archipelago(constructor(sade(gen=g),n)))
self.new_topology(
desc='{}, ihs'.format(desc),
category=category,
algorithms=['ihs'],
archipelago=Archipelago(constructor(ihs(gen=g),n)))
self.new_topology(
desc='{}, pso'.format(desc),
category=category,
algorithms=['pso'],
archipelago=Archipelago(constructor(pso(gen=g),n)))
self.new_topology(
desc='{}, pso_gen'.format(desc),
category=category,
algorithms=['pso_gen'],
archipelago=Archipelago(constructor(pso_gen(gen=g),n)))
# self.new_topology(
# desc='{}, simulated_annealing'.format(desc),
# category=category,
# algorithms=['simulated_annealing'],
# archipelago=Archipelago(constructor(simulated_annealing(),n)))
self.new_topology(
desc='{}, bee_colony'.format(desc),
category=category,
algorithms=['bee_colony'],
archipelago=Archipelago(constructor(bee_colony(gen=g),n)))
self.new_topology(
desc='{}, cmaes'.format(desc),
category=category,
algorithms=['cmaes'],
archipelago=Archipelago(constructor(cmaes(gen=g),n)))
self.new_topology(
desc='{}, nsga2'.format(desc),
category=category,
algorithms=['nsga2'],
archipelago=Archipelago(constructor(nsga2(gen=g),n)))
self.new_topology(
desc='{}, xnes'.format(desc),
category=category,
algorithms=['xnes'],
archipelago=Archipelago(constructor(xnes(gen=g),n)))
# de + nelder mead combo
self.new_topology(
desc='{}, de+nelder mead'.format(desc),
category=category,
algorithms=['de','neldermead'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), self.make_nelder_mead()))
# de + praxis combo
self.new_topology(
desc='{}, de+praxis'.format(desc),
category=category,
algorithms=['de','praxis'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), self.make_praxis()))
# de + nsga2 combo
self.new_topology(
desc='{}, de+nsga2'.format(desc),
category=category,
algorithms=['de','nsga2'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), nsga2(gen=g)))
# de + de1220 combo
self.new_topology(
desc='{}, de+de1220'.format(desc),
category=category,
algorithms=['de','de1220'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), de1220(gen=g)))
# de + sade combo
self.new_topology(
desc='{}, de+sade'.format(desc),
category=category,
algorithms=['de','sade'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), sade(gen=g)))
# de + pso combo
self.new_topology(
desc='{}, de+pso'.format(desc),
category=category,
algorithms=['de','pso'],
archipelago=assign_2nd_alg(Archipelago(constructor(de(gen=g),n)), pso(gen=g)))
# extra configurations for fully connected topology
if constructor is self.factory.createFullyConnected:
self.new_topology(
desc='{}, de+pso+praxis'.format(desc),
category=category,
algorithms=['de','pso','praxis'],
archipelago=assign_algs(Archipelago(constructor(de(gen=g),n)), (de(gen=g), pso(gen=g), self.make_praxis())))
self.new_topology(
desc='{}, de+pso+praxis+nsga2'.format(desc),
category=category,
algorithms=['de','pso','praxis','nsga2'],
archipelago=assign_algs(Archipelago(constructor(de(gen=g),n)), (de(gen=g), pso(gen=g), self.make_praxis(), nsga2(gen=g))))
self.new_topology(
desc='{}, de+pso+praxis+cmaes'.format(desc),
category=category,
algorithms=['de','pso','praxis','cmaes'],
archipelago=assign_algs(Archipelago(constructor(de(gen=g),n)), (de(gen=g), pso(gen=g), self.make_praxis(), cmaes(gen=g))))
self.new_topology(
desc='{}, de+pso+praxis+xnes'.format(desc),
category=category,
algorithms=['de','pso','praxis','xnes'],
archipelago=assign_algs(Archipelago(constructor(de(gen=g),n)), (de(gen=g), pso(gen=g), self.make_praxis(), xnes(gen=g))))