pflacco.local_optima_network_features#

pflacco.local_optima_network_features.calculate_lon_features(nodes: DataFrame, edges: DataFrame, f_opt: Optional[float] = None) → Dict[str, Optional[Union[int, float]]]#

Calculation of Local Optima Network (LON) features based on a LON.

Parameters

  • nodes (pd.DataFrame) – A dataframe containing all nodes of the LON. Can be created via the function compute_local_optima_network.

  • edges (pd.DataFrame) – A dataframe containg all edges of the LON. Can be created via the function compute_local_optima_network.

  • f_opt (float, optional) – Objective value of the global optimum (if known) of the objective function the LON was computed on, by default None.

Returns

Dictionary consisting of the calculated features.

Return type

Dict[str, Optional[Union[int, float]]]

pflacco.local_optima_network_features.compute_local_optima_network(f: Callable[[List[float]], float], dim: int, lower_bound: Union[List[float], float], upper_bound: Union[List[float], float], seed: Optional[int] = None, stepsize: int = 2, basin_hopping_iteration: int = 100, stopping_threshold: int = 1000, minimizer_kwargs: Optional[Dict[str, Any]] = None) → Tuple[DataFrame, DataFrame]#

Computation of a Local Optima Network in accordance to [1].

Parameters

  • f (Callable[[List[float]], float]) – Objective function to be optimized.

  • dim (int) – Dimensionality of the decision space.

  • lower_bound (Union[List[float], float]) – Lower bound of variables of the decision space.

  • upper_bound (Union[List[float], float]) – Upper bound of variables of the decision space.

  • seed (Optional[int], optional) – Seed for reproducability, by default None.

  • stepsize (int, optional) – Maximum step size for use in the random displacement, by default 2

  • basin_hopping_iteration (int, optional) – Number of independet basin-hopping runs. Each basin-hopping run consists of multiple iterations of a local search method, by default 100.

  • stopping_threshold (int, optional) – Number of basin-hopping iterations. There will be a total of basin_hopping_iteration + 1 runs of the local minimizer, by default 1000

  • minimizer_kwargs (Optional[Dict[str, Any]], optional) – Extra keyword arguments to be passed to the local minimizer scipy.optimize.minimize, by default None.

Returns

A tuple consisting of two Pandas dataframes. The first dataframe contains information about the nodes, i.e., the local optima. The second dataframe contains information about the edges.

Return type

Tuple[pd.DataFrame, pd.DataFrame]

References

[1] Adair, J., Ochoa, G. and Malan, K.M., 2019, July.

Local optima networks for continuous fitness landscapes. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1407-1414).