Building my own EDA implementation

In this notebook we show how the EDA can be implemented in a modular way using the components available in EDAspy. This way, the user is able to build implementations that may not be considered in the state-of-the-art. EDASpy also has the implementations of typical EDA implementations used in the state-of-the-art.

We first import from EDAspy all the needed functions and classes. To build our own EDA we use a modular class that extends the abstract class of EDA used as a baseline of all the EDA implementations in EDAspy.

from EDAspy.optimization.custom import EDACustom, GBN, UniformGenInit
from EDAspy.benchmarks import ContinuousBenchmarkingCEC14

We initialize an object with the EDACustom object. Note that, independently of the pm and init parameteres, we are goind to overwrite these with our own objects. If not, we have to choose which is the ID of the pm and init we want.

n_variables = 10
my_eda = EDACustom(size_gen=100, max_iter=100, dead_iter=n_variables, n_variables=n_variables, alpha=0.5,
                   elite_factor=0.2, disp=True, pm=4, init=4, bounds=(-50, 50))

benchmarking = ContinuousBenchmarkingCEC14(n_variables)

We now implement our initializator and probabilistic model and add these to our EDA.

my_gbn = GBN([str(i) for i in range(n_variables)])
my_init = UniformGenInit(n_variables) = my_gbn
my_eda.init = my_init

We run our EDA in one of the benchmarks that is implemented in EDAspy.

eda_result = my_eda.minimize(cost_function=benchmarking.cec14_4)

We can access the results in the result object: