gomea 1.0.4

Library for the use of various variants of the Gene-pool Optimal Mixing Evolutionary Algorith (GOMEA).

README

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:

• Bouter, A., & Bosman, P.A.N. (2023). A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA. In Proceedings of the Genetic and Evolutionary Computation Conference Companion

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.

INSTALL

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

RUN

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.