Data for EMO2023 Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"

<p><strong>Data for Paper &quot;Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective&quot;</strong></p> <p><br> The file <strong>dbmopp_dataset_perf.csv</strong> contains results from the 945 x 30 instances, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>instance_id</em>: instance (fold) identifier {1, ..., 30}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume</em>: hypervolume reached by the algorithm [0.0, 1.0]</li> </ul> <p>&nbsp;</p> <p>The file <strong>dbmopp_dataset_perf_aggregated.csv</strong> contains average results from the 945 problems, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume_avg</em>: average hypervolume reached by the algorithm [0.0, 1.0]</li> <li><em>best</em>: 1 if the corresponding algorithm obtains the best average hypervolume, 0 otherwise</li> </ul> <p>&nbsp;</p>