Genetic Algorithms (GA)

class pypop7.optimizers.ga.ga.GA(problem, options)[source]

Genetic Algorithm (GA).

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

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 setting (key):

    • ’n_individuals’ - population size (int, default: 100).

n_individuals

population size.

Type:

int

References

Whitley, D., 2019. Next generation genetic algorithms: A user’s guide and tutorial. In Handbook of Metaheuristics (pp. 245-274). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-91086-4_8

De Jong, K.A., 2006. Evolutionary computation: A unified approach. MIT Press. https://mitpress.mit.edu/9780262041942/evolutionary-computation/

Mitchell, M., 1998. An introduction to genetic algorithms. MIT Press. https://mitpress.mit.edu/9780262631853/an-introduction-to-genetic-algorithms/

Levine, D., 1997. Commentary—Genetic algorithms: A practitioner’s view. INFORMS Journal on Computing, 9(3), pp.256-259. https://pubsonline.informs.org/doi/10.1287/ijoc.9.3.256

Goldberg, D.E., 1994. Genetic and evolutionary algorithms come of age. Communications of the ACM, 37(3), pp.113-120. https://dl.acm.org/doi/10.1145/175247.175259

De Jong, K.A., 1993. Are genetic algorithms function optimizer?. Foundations of Genetic Algorithms, pp.5-17. https://www.sciencedirect.com/science/article/pii/B9780080948324500064

Forrest, S., 1993. Genetic algorithms: Principles of natural selection applied to computation. Science, 261(5123), pp.872-878. https://www.science.org/doi/10.1126/science.8346439

Mitchell, M., Holland, J. and Forrest, S., 1993. When will a genetic algorithm outperform hill climbing. Advances in Neural Information Processing Systems (pp. 51-58). https://proceedings.neurips.cc/paper/1993/hash/ab88b15733f543179858600245108dd8-Abstract.html

Holland, J.H., 1992. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT press. https://direct.mit.edu/books/book/2574/Adaptation-in-Natural-and-Artificial-SystemsAn

Holland, J.H., 1992. Genetic algorithms. Scientific American, 267(1), pp.66-73. https://www.scientificamerican.com/article/genetic-algorithms/

Goldberg, D.E., 1989. Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley. https://www.goodreads.com/en/book/show/142613

Goldberg, D.E. and Holland, J.H., 1988. Genetic algorithms and machine learning. Machine Learning, 3(2), pp.95-99. https://link.springer.com/article/10.1023/A:1022602019183

Holland, J.H., 1973. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), pp.88-105. https://epubs.siam.org/doi/10.1137/0202009

Holland, J.H., 1962. Outline for a logical theory of adaptive systems. Journal of the ACM, 9(3), pp.297-314. https://dl.acm.org/doi/10.1145/321127.321128

GAs: