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.
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.
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. (Pedro Larrañaga + Jose Lozano: IEEE Fellows 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.
EDAs: