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

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

The file dbmopp_dataset_perf.csv contains results from the 945 x 30 instances, with the following columns:

design_id: problem identifier

n_var: number of variables {2, ..., 20}

n_obj: number of objectives {2, ..., 10}

nonident_ps: non-identical Pareto sets {0 (no), 1 (yes)}

var_density: varying density {0 (no), 1 (yes)}

n_discon_ps: number of disconnected Pareto sets {0, ..., 6}

n_local_fronts: number of local fronts {0, ..., 6}

n_resist_regions: number of dominance resistance regions {0, ..., 6}

instance_id: instance (fold) identifier {1, ..., 30}

budget: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}

algo: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}

hypervolume: hypervolume reached by the algorithm [0.0, 1.0]

The file dbmopp_dataset_perf_aggregated.csv contains average results from the 945 problems, with the following columns:

design_id: problem identifier

n_var: number of variables {2, ..., 20}

n_obj: number of objectives {2, ..., 10}

nonident_ps: non-identical Pareto sets {0 (no), 1 (yes)}

var_density: varying density {0 (no), 1 (yes)}

n_discon_ps: number of disconnected Pareto sets {0, ..., 6}

n_local_fronts: number of local fronts {0, ..., 6}

n_resist_regions: number of dominance resistance regions {0, ..., 6}

budget: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}

algo: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}

hypervolume_avg: average hypervolume reached by the algorithm [0.0, 1.0]

best: 1 if the corresponding algorithm obtains the best average hypervolume, 0 otherwise