Estimation of Distribution Algorithms (EDA)

class pypop7.optimizers.eda.eda.EDA(problem, options)[source]

Estimation of Distribution Algorithms (EDA).

This is the abstract class for all EDA classes. Please use any of its instantiated subclasses to optimize the black-box problem at hand.


`EDA` are a modern branch of evolutionary algorithms with some unique advantages in principle, as recognized in [Kabán et al., 2016, ECJ].

AKA probabilistic model-building genetic algorithms (PMBGA), iterated density estimation evolutionary algorithms (IDEA).

  • problem (dict) –

    problem arguments with the following common settings (keys):
    • ’fitness_function’ - objective function to be minimized (func),

    • ’ndim_problem’ - number of dimensionality (int),

    • ’upper_boundary’ - upper boundary of search range (array_like),

    • ’lower_boundary’ - lower boundary of search range (array_like).

  • options (dict) –

    optimizer options with the following common settings (keys):
    • ’max_function_evaluations’ - maximum of function evaluations (int, default: np.Inf),

    • ’max_runtime’ - maximal runtime to be allowed (float, default: np.Inf),

    • ’seed_rng’ - seed for random number generation needed to be explicitly set (int);

    and with the following particular settings (keys):
    • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 200),

    • ’n_parents’ - number of parents, aka parental population size (int, default: int(self.n_individuals/2)).


number of offspring, aka offspring population size.




number of parents, aka parental population size.




Brookes, D., Busia, A., Fannjiang, C., Murphy, K. and Listgarten, J., 2020, July. A view of estimation of distribution algorithms through the lens of expectation-maximization. In Proceedings of Genetic and Evolutionary Computation Conference Companion (pp. 189-190). ACM.

Kabán, A., Bootkrajang, J. and Durrant, R.J., 2016. Toward large-scale continuous EDA: A random matrix theory perspective. Evolutionary Computation, 24(2), pp.255-291.

Larrañaga, P. and Lozano, J.A. eds., 2002. Estimation of distribution algorithms: A new tool for evolutionary computation. Springer Science & Business Media. ([Jose Lozano: IEEE Fellow for contributions to EDAs](

Mühlenbein, H. and Mahnig, T., 2001. Evolutionary algorithms: From recombination to search distributions. In Theoretical Aspects of Evolutionary Computing (pp. 135-173). Springer, Berlin, Heidelberg.

Berny, A., 2000, September. Selection and reinforcement learning for combinatorial optimization. In International Conference on Parallel Problem Solving from Nature (pp. 601-610). Springer, Berlin, Heidelberg.

Bosman, P.A. and Thierens, D., 2000, September. Expanding from discrete to continuous estimation of distribution algorithms: The IDEA. In International Conference on Parallel Problem Solving from Nature (pp. 767-776). Springer, Berlin, Heidelberg.

Mühlenbein, H., 1997. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3), pp.303-346.

Baluja, S. and Caruana, R., 1995. Removing the genetics from the standard genetic algorithm. In International Conference on Machine Learning (pp. 38-46). Morgan Kaufmann.