“…The raw regression data has been collected using Python implementation, while the personalized approach and all the evaluations have been performed using R. Our fixed-budget regression is inspired by [21], but applied here to the more diverse set of algorithms suggested in [24]. Concretely, we aim at predicting the performance of the following 12 algorithms: BrentSTEPqi [36], BrentSTEPrr [36], CMA-ES-CSA [1], HCMA [26], HMLSL [33], IPOP400D [2], MCS [19], MLSL [33], OQNLP [34], fmincon [34], fminunc [34], and BIPOP-CMA-ES [13]. Note here that the latter does not appear in the portfolio analyzed in [24], but it was added since for one algorithm from the original study the raw performance data was missing.…”