activity
20182023
most citedCombining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems

49 citations · 63 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NE2023

Computational and Exploratory Landscape Analysis of the GKLS Generator

Jakub Kudela, Martin Juricek

The GKLS generator is one of the most used testbeds for benchmarking global optimization algorithms. In this paper, we conduct both a computational analysis and the Exploratory Lan…

cs.NE2023★ 9 cited

The Evolutionary Computation Methods No One Should Use

Jakub Kudela

The center-bias (or zero-bias) operator has recently been identified as one of the problems plaguing the benchmarking of evolutionary computation methods. This operator lets the me…

cs.NE2022★ 1 cited

Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems

Jakub Kudela

In the field of derivative-free optimization, both of its main branches, the deterministic and nature-inspired techniques, experienced in recent years substantial advancement. In t…

cs.NE2022★ 49 cited

Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems

Jakub Kudela, Radomil Matousek

Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many re…

cs.CL2018★ 4 cited

Extracting Parallel Paragraphs from Common Crawl

Jakub Kúdela, Irena Holubová, Ondřej Bojar

Most of the current methods for mining parallel texts from the web assume that web pages of web sites share same structure across languages. We believe that there still exists a no…