deephyper.skopt.optimizer.acq_optimizer.pymoo_ga.minimize

Contents

deephyper.skopt.optimizer.acq_optimizer.pymoo_ga.minimize#

deephyper.skopt.optimizer.acq_optimizer.pymoo_ga.minimize(problem, algorithm, termination=None, copy_algorithm=True, copy_termination=True, **kwargs)[source]#

Minimization of function of one or more variables, objectives and constraints.

This is used as a convenience function to execute several algorithms with default settings which turned out to work for a test single. However, evolutionary computations utilizes the idea of customizing a meta-algorithm. Customizing the algorithm using the object-oriented interface is recommended to improve the convergence.

Parameters:

  • problem (Problem) – A problem object which is defined using pymoo.

  • algorithm (Algorithm) – The algorithm object that should be used for the optimization.

  • termination (Termination or tuple) – The termination criterion that is used to stop the algorithm.

  • seed (integer) – The random seed to be used.

  • verbose (bool) – Whether output should be printed or not.

  • display (Display) – Each algorithm has a default display object for printouts. However, it can be overwritten if desired.

  • callback (Callback) – A callback object which is called each iteration of the algorithm.

  • save_history (bool) – Whether the history should be stored or not.

  • copy_algorithm (bool) – Whether the algorithm object should be copied before optimization.

Returns:

res – The optimization result represented as an object.

Return type:

Result

Contents