Evolutionary Programming (EP)

class pypop7.optimizers.ep.ep.EP(problem, options)[source]

Evolutionary Programming (EP).

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

Note

EP is one of three classical families of evolutionary algorithms (EAs), proposed originally by Lawrence J. Fogel, the recipient of IEEE Evolutionary Computation Pioneer Award 1998 and IEEE Frank Rosenblatt Award 2006. When used for continuous optimization, most of modern EP versions share much similarities (e.g. self-adaptation) with ES, another representative EA.

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):

    • ’sigma’ - initial global step-size, aka mutation strength (float),

    • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 100).

n_individuals

number of offspring, aka offspring population size.

Type:

int

sigma

initial global step-size, aka mutation strength.

Type:

float

References

Lee, C.Y. and Yao, X., 2004. Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Transactions on Evolutionary Computation, 8(1), pp.1-13. https://ieeexplore.ieee.org/document/1266370

Yao, X., Liu, Y. and Lin, G., 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), pp.82-102. https://ieeexplore.ieee.org/abstract/document/771163

Fogel, D.B., 1999. An overview of evolutionary programming. In Evolutionary Algorithms (pp. 89-109). Springer, New York, NY. https://link.springer.com/chapter/10.1007/978-1-4612-1542-4_5

Fogel, D.B. and Fogel, L.J., 1995, September. An introduction to evolutionary programming. In European Conference on Artificial Evolution (pp. 21-33). Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/3-540-61108-8_28

Fogel, D.B., 1994. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 5(1), pp.3-14. https://ieeexplore.ieee.org/abstract/document/265956

Fogel, D.B., 1994. Evolutionary programming: An introduction and some current directions. Statistics and Computing, 4(2), pp.113-129. https://link.springer.com/article/10.1007/BF00175356

Bäck, T. and Schwefel, H.P., 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), pp.1-23. https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for

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