How to use external application instance in a pymoo problem without needing to serialise it to enable saving history?

Question

I'm trying to include an external application instance in the evaluation of a pymoo problem. I've got this working, but having the external application in the pymoo problem means that I can't use the save_history method, and I get the following error:

>> RuntimeError: Pickling of "arcog.Application" instances is not enabled (http://www.boost.org/libs/python/doc/v2/pickle.html)

I'm aiming to be able to use the save_history method without saving the external application instance

The evaluate function I need requires this external application instance to work - as such, I'm looking for a solution that will allow me to use the save_history method without saving the external application instance (or even the Problem itself..)

I've tried:

• Using a wrapper function outside of the ProblemWrapper class (could have implemented this incorrectly) - this still meant that the external application needed to be serialised, and I get the RuntimeError.
• I've tried using save_algorithm=False, in minimize, but the Problem itself still gets saved, and I get the RuntimeError.

The associated code snippet is below:

from pymoo.core.problem import Problem
from pymoo.optimize import minimize
from pymoo.algorithms.moo.nsga2 import NSGA2


class ProblemWrapper(Problem):
        def __init__(self, app, xconfig, limits, *args, **kwargs):
            Problem.__init__(self,  *args, **kwargs)
            self.app = app
            self.xconfig = xconfig
            self.limits = limits

        def _evaluate(self, designs, out, *args, **kwargs):
            res = []
            for design in designs:
                print("Evaluating an individual.")
                evald = eval_func(design, self.app,
                                  self.xconfig, self.limits)
                res.append(evald)
            out["F"] = np.array(res)


problem = ProblemWrapper(n_var=N_VAR, n_obj=N_OBJ, xl=xl,
                         xu=xu, app=app,
                         xconfig=xconfig, limits=limits)

algorithm = NSGA2(pop_size=POPSIZE)
stop_criteria = ('n_gen', N_GEN)
results = minimize(
            problem=problem,
            algorithm=algorithm,
            termination=stop_criteria,
            save_history=True)

Thanks in advance for your advice :) I'm sensing there's an opportunity to learn some new fundamentals here, but I'm not quite onto it enough to know what to look for.