Estimation of Distribution Algorithms (EDA)

class pypop7.optimizers.eda.eda.EDA(problem, options)

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.

Note

`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).

Parameters:

  • 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)).

n_individuals

number of offspring, aka offspring population size.

Type:

int

n_parents

number of parents, aka parental population size.

Type:

int

References

https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=22182

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. https://dl.acm.org/doi/abs/10.1145/3377929.3389938

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. https://direct.mit.edu/evco/article-abstract/24/2/255/1016/Toward-Large-Scale-Continuous-EDA-A-Random-Matrix

Larrañaga, P. and Lozano, J.A. eds., 2002. Estimation of distribution algorithms: A new tool for evolutionary computation. Springer Science & Business Media. https://link.springer.com/book/10.1007/978-1-4615-1539-5 ([Jose Lozano: IEEE Fellow for contributions to EDAs](https://tinyurl.com/sssfsfw8))

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. https://link.springer.com/chapter/10.1007/978-3-662-04448-3_7

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. https://link.springer.com/chapter/10.1007/3-540-45356-3_59

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. https://link.springer.com/chapter/10.1007/3-540-45356-3_75

Mühlenbein, H., 1997. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3), pp.303-346. https://tinyurl.com/yt78c786

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. https://www.sciencedirect.com/science/article/pii/B9781558603776500141

EDAs: