Evaluating Real-World Generalizability of Algorithm Selection Models This Zenodo upload contains: Raw benchmark results (per algorithm, per dimension, per benchmark) Instance sample files used to compute ELA features Computed ELA feature tables Aggregated performance tables used in the algorithm-selection experiments Crossmatch test CSVs for distribution-comparison analyses 1. Top-level layout At the top level you will find four benchmark folders, one folder with ELA features, several aggregated performance tables, and CSVs for crossmatch analyses: BBOB/ Raw results + samples for BBOB benchmarkCEC/ Raw results + samples for CEC benchmarkROB/ Raw results + samples for ROB benchmarkUAV/ Raw results + samples for UAV benchmarkela/ Computed ELA feature tables (CSV) performance_dim_6_noPRS.csvperformance_dim_6_no_agg_noPRS.csvperformance_dim_12_noPRS.csvperformance_dim_12_no_agg_noPRS.csvperformance_dim_18_noPRS.csvperformance_dim_18_no_agg_noPRS.csvperformance_dim_24_noPRS.csvperformance_dim_24_no_agg_noPRS.csvperformance_dim_30_noPRS.csvperformance_dim_30_no_agg_noPRS.csv crossmatch_test.csvcrossmatch_test_cosine.csvcrossmatch_test_euclidean.csv .ipynb_checkpoints/ (optional) notebook metadata folder Top-level files explained A) performance tables performance_dim_{d}_noPRS.csvAggregated performance table for dimension d (mean aggregated over runs), excluding PRS. performance_dim_{d}_no_agg_noPRS.csvNon-aggregated performance table for dimension d (keeps per-run information), excluding PRS. B) crossmatch tables crossmatch_test*.csvCSVs used for/produced by crossmatch-style distribution comparison tests (including cosine and euclidean variants). 2. Benchmark folders (ROB/, BBOB/, UAV/, CEC/) Each benchmark folder follows the same structure and naming conventions. Inside each of ROB/, BBOB/, UAV/, CEC/ you typically find: _readme Short benchmark-specific notes (if present) .ipynb_checkpoints/ (optional) res__dim_samples.csv res__dimbudget20000.csv (multiple algorithms) File types inside each benchmark folder A) Instance samples (input for ELA) res__dim_samples.csvInstance sample data used to compute ELA features for benchmark in dimension .These files are the inputs to the ELA computation step. B) Raw algorithm results res__dimbudget20000.csvRaw performance results for a specific algorithm on benchmark , dimension ,with evaluation budget 20000. Placeholders: is one of: ROB, BBOB, UAV, CEC is the problem dimension (e.g., 6, 12, 18, 24, 30) is the algorithm identifier (e.g., DE, PSO, LSHADE, etc.) 3. ELA features folder (ela/) The ela/ directory contains computed ELA feature tables generated from the res_*_samples.csv files. Typical naming convention: dimscaledownsample.csv Where: indicates the scaling mode used during feature generation (0, 1, or y) indicates whether downsampling was applied (0/1) 4. AS_results_* folders The AS_results folders contain the outputs of the algorithm selection experiments (produced by 2_AS.py). Files are saved per experimental setting, where the setting is encoded in the filename. Each result file name follows the pattern: TYPE_dim_scaledownsample.csv Where: TYPE indicates what is stored in the file: final_results: aggregated evaluation metrics and summary results for the AS experiment predictions: per-instance predictions (e.g., selected algorithm / predicted best / scores depending on the run) feature_importances: feature importance values from the trained model(s) d is the problem dimension (e.g., 6, 12, 18, 24, 30) S is the scaling mode used for the features/targets: 0 = no scaling 1 = min–max scaling of X and y y = min–max scaling of y only downsample indicates whether downsampling was applied to the instance samples before computing ELA features: True = downsampling applied False = no downsampling Typical files inside AS_results final_results_dim_scaledownsample.csvSummary metrics for the experiment setting (one row per model/strategy or per split, depending on configuration). predictions_dim_scaledownsample.csvInstance-level outputs for the experiment setting (e.g., chosen algorithm and/or predicted performance). feature_importances_dim_scaledownsample.csvFeature importance values computed from the trained model(s) for that setting. 5. Relationship between files (high level) Benchmark sample files:/res__dim_samples.csvare used to compute ELA feature tables in:ela/dimscaledownsample.csv Benchmark raw algorithm result files:/res_dimbudget20000.csvare aggregated into the top-level performance tables:performance_dim{d}noPRS.csv and performance_dim{d}_no_agg_noPRS.csv crossmatch_test*.csv are used for distribution-comparison / crossmatch analyses across datasets or feature spaces