Thank you. I encountered the following problem.
Traceback (most recent call last):
File " in
ea.optimize(solver = "cpso", sync = True)
File "/site-packages/stochopy/evolutionary_algorithm.py", line 293, in optimize
xstart = xstart, sync = sync)
File "/stochopy/evolutionary_algorithm.py", line 755, in _cpso
pbest[idx] = np.array(X[idx])
IndexError: boolean index did not match indexed array along dimension 1; dimension is 80 but corresponding boolean dimension is 1

The execution is as follows

 func = "rastrigin"
# mpi_comm = MPI.COMM_WORLD
# mpi_rank = mpi_comm.Get_rank()    
n_dim = 80 
popsize = 4 + np.floor(3.*np.log(n_dim))

lower = np.full(n_dim, -40)
upper = np.full(n_dim, -20)

# Initialize solver; calc_cost calculates the residual after running a FEM problem.
ea = Evolutionary(calc_cost, lower = lower, upper = upper, eps1 = 1e-2, popsize = popsize,  max_iter = 2000, mpi = False, random_state = -1)

# Solve
starttime = time()
ea.optimize(solver = "cpso", sync = True)    
print(ea)
print("Elapsed time: %.2f seconds" % (time() - starttime))

I see you have written optimization code using Python and Differential Evolution. Scipy has solvers for non-linear equations, however scipy's default initial parameter values are all 1.0. This can be suboptimal, and in more complex equations often results in the algorithms finding a local minimum in error space. For this reason, the authors of scipy added the Differential Evolution genetic algorithm for initial parameter estimation for use in optimization. The module is named scipy.optimize.differential_evolution.

Because their implementation uses the Latin Hypercube algorithm to ensure a thorough search of parameter space, this particular scipy module requires parameter bounds.

I have used scipy's Differential Evolution genetic algorithm to determine initial parameters for fitting a double Lorentzian peak equation to Raman spectroscopy data of carbon nanotubes and found that the results were excellent. The GitHub project, with a test spectroscopy data file, is:

https://github.com/zunzun/RamanSpectroscopyFit

My background is in nuclear engineering and industrial radiation physics, and I love Python, so if you have any questions please let me know.

James Phillips

Hi keurfonluu,

Thank you for sharing such a wonderful package!

I try to use the your dispersion inversion code, and find that there is a problem with:
from stochopy import Evolutionary
ImportError: cannot import name 'Evolutionary' from 'stochopy' (~/.conda/envs/pg/lib/python3.8/site-packages/stochopy/__init__.py)

I installed evodcinv with pip. and the version of stochopy is (2.0.1)

Do you know where this problem might come from?

Thanks in advance.

Kind regards,
Wen

Hi.
Can stochOpy be used for hyper-parameters just like in the Bayesian-Optimization package?
For example I want to find the optimal C parameter from SVR regression sklearn that can minimize MAE objective function.
So the example is as follows:

dicts={'params':{'C':[-1e3,1e3]}}
def MAE_func(**param):
    model.set_params(**param)
    model.fit(train, y_train)
 
cv = Evolutionary(MAE_func,lower=lower,upper=upper,max_iter = 200,snap = True)
xopt, gfit = cv.optimize(solver = "cpso")

But it returns an error:

File "/home/lemma/train.py", line 1998, in optimize_genetic1
xopt, gfit = cv.optimize(solver = "cpso")
File "/home/lemma/Evo.py", line 279, in optimize
xstart = xstart, sync = sync)
File "/home/lemma/Evo.py", line 666, in _cpso
pfit = self._eval_models(X)
File "/home/lemma/Evo.py", line 306, in _eval_models
fit_mpi[i] = self._func(self._unstandardize(models[i,:]))
File "/home/lemma/Evo.py", line 78, in
self._func = lambda x: func(x, *args, **kwargs)
TypeError: fun() takes exactly 0 arguments (1 given)

Not sure what went wrong with this?