The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a state-of-the-art Model-Based Evolutionary Algorithm (MBEA) with an ongoing line of research into various domains of (evolutionary) optimization. The initial release of the GOMEA library (v1.0.0) is described in the publication:
The current release of the GOMEA library supports optimization in the following domains:
- Discrete single-objective optimization
- Real-valued single-objective optimization
Support for multi-objective optimization in both of these domains is planned for a future release.
Relevant most recent publications are the following:
- Dushatskiy, A., Virgolin, M., Bouter, A., Thierens, D., & Bosman, P.A.N. (2021). Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms. arXiv preprint arXiv:2109.05259.
- Bouter, A., Alderliesten, T., & Bosman, P.A.N. (2021). Achieving highly scalable evolutionary real-valued optimization by exploiting partial evaluations. Evolutionary computation, 29(1), 129-155.
The most straightforward installation option is through pip, as follows.
pip install gomea
To build from source, execute the following in the root GOMEA folder.
make install
Running a simple benchmark problem, e.g., the Rosenbrock function, can be done as follows:
import gomea
frv = gomea.fitness.RosenbrockFunction(10,value_to_reach=1e-10)
lm = gomea.linkage.Univariate()
rvgom = gomea.RealValuedGOMEA(fitness=frv, linkage_model=lm, lower_init_range=-115, upper_init_range=-100)
result = rvgom.run()
Plotting a simple convergence plot can then be done using:
import matplotlib.pyplot as plt
plt.grid()
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Number of evaluations')
plt.ylabel('Best objective value')
plt.plot(result['evaluations'],result['best_obj_val'])
Rather than using a predefined fitness function, it is also possible to define your own custom fitness function for Black-Box Optimization (BBO) or Gray-Box Optimization (GBO). The definition of a custom (real-valued) BBO function can be done as follows.
class CustomRosenbrockFunction(gomea.fitness.BBOFitnessFunctionRealValued):
def objective_function( self, objective_index, variables ):
f = 0
for i in range(len(variables)-1):
x = variables[i]
y = variables[i+1]
f += 100*(y-x*x)*(y-x*x) + (1.0-x)*(1.0-x)
return f
dim = 10
f_rosenbrock = CustomRosenbrockFunction(dim,value_to_reach=1e-10)
The definition of a GBO function requires defining the input and output of each subfunction. Thorough definitions of this are given in the EvoSOFT workshop paper. An example of the Rosenbrock function defined in a GBO setting is as follows:
class CustomRosenbrockFunction(gomea.fitness.GBOFitnessFunctionRealValued):
def number_of_subfunctions( self ):
return self.number_of_variables-1
def inputs_to_subfunction( self, subfunction_index ):
return [subfunction_index, subfunction_index+1]
def subfunction(self, subfunction_index, variables):
x = variables[subfunction_index]
y = variables[subfunction_index+1]
return 100*(y-x*x)*(y-x*x) + (1.0-x)*(1.0-x)
dim = 10
f_rosenbrock = CustomRosenbrockFunction(dim,value_to_reach=1e-10)
Further examples are given in the demos directory.