Cooperative Coevolution (CC)

class pypop7.optimizers.cc.cc.CC(problem, options)[source]

Cooperative Coevolution (CC).

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

Note

CC uses the decomposition strategy to alleviate curse-of-dimensionality for large-scale black-box optimization. Refer to [Panait et al., 2008, JMLR] for convergence analyses and e.g. [Gomez et al.,, 2008, JMLR] for state-of-the-art neuroevolution applications from Schmidhuber and/or Miikkulainen’s lab.

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’ - number of individuals/samples, aka population size (int, default: 100).

References

Gomez, F., Schmidhuber, J. and Miikkulainen, R., 2008. Accelerated neural evolution through cooperatively coevolved synapses. Journal of Machine Learning Research, 9(31), pp.937-965. https://www.jmlr.org/papers/v9/gomez08a.html

Panait, L., Tuyls, K. and Luke, S., 2008. Theoretical advantages of lenient learners: An evolutionary game theoretic perspective. Journal of Machine Learning Research, 9, pp.423-457. https://jmlr.org/papers/volume9/panait08a/panait08a.pdf

Schmidhuber, J., Wierstra, D., Gagliolo, M. and Gomez, F., 2007. Training recurrent networks by evolino. Neural Computation, 19(3), pp.757-779. https://direct.mit.edu/neco/article-abstract/19/3/757/7156/Training-Recurrent-Networks-by-Evolino

Gomez, F.J. and Schmidhuber, J., 2005, June. Co-evolving recurrent neurons learn deep memory POMDPs. In Proceedings of Annual Conference on Genetic and Evolutionary Computation (pp. 491-498). ACM. https://dl.acm.org/doi/10.1145/1068009.1068092

Fan, J., Lau, R. and Miikkulainen, R., 2003. Utilizing domain knowledge in neuroevolution. In International Conference on Machine Learning (pp. 170-177). https://www.aaai.org/Library/ICML/2003/icml03-025.php

Potter, M.A. and De Jong, K.A., 2000. Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1), pp.1-29. https://direct.mit.edu/evco/article/8/1/1/859/Cooperative-Coevolution-An-Architecture-for

Gomez, F.J. and Miikkulainen, R., 1999, July. Solving non-Markovian control tasks with neuroevolution. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 1356-1361). https://www.ijcai.org/Proceedings/99-2/Papers/097.pdf

Moriarty, D.E. and Mikkulainen, R., 1996. Efficient reinforcement learning through symbiotic evolution. Machine Learning, 22(1), pp.11-32. https://link.springer.com/article/10.1023/A:1018004120707

Moriarty, D.E. and Miikkulainen, R., 1995. Efficient learning from delayed rewards through symbiotic evolution. In International Conference on Machine Learning (pp. 396-404). Morgan Kaufmann. https://www.sciencedirect.com/science/article/pii/B9781558603776500566

Potter, M.A. and De Jong, K.A., 1994, October. A cooperative coevolutionary approach to function optimization. In International Conference on Parallel Problem Solving from Nature (pp. 249-257). Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/3-540-58484-6_269

CC: