no code implementations • 11 Apr 2024 • Konstantin Dietrich, Diederick Vermetten, Carola Doerr, Pascal Kerschke
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite.
no code implementations • 2 Jan 2024 • Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann
As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA.
no code implementations • 16 Feb 2023 • Vanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tusar
This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.
1 code implementation • 26 Nov 2022 • Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke, Marco F. Huber
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT).
1 code implementation • 30 Jul 2022 • Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke
We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems.
1 code implementation • 22 Apr 2022 • Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
It is able to efficiently model and exploit LE sets in MMMOO problems.
no code implementations • 12 Apr 2022 • Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke, Heike Trautmann
The quality of our approaches is on par with methods relying on the traditional landscape features.
1 code implementation • 29 Nov 2020 • Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Simultaneously visualizing the decision and objective space of continuous multi-objective optimization problems (MOPs) recently provided key contributions in understanding the structure of their landscapes.
no code implementations • 2 Oct 2020 • Vera Steinhoff, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Christian Grimme
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress.
no code implementations • 7 Jul 2020 • Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
1 code implementation • 29 Jun 2020 • Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, Heike Trautmann
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS).
no code implementations • 25 Jun 2020 • Vera Steinhoff, Pascal Kerschke, Christian Grimme
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization.
1 code implementation • 20 Jun 2020 • Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Therefore, we build on the GFH approach but apply a new technique for approximating the location of locally efficient points and using the divergence of the multi-objective gradient vector field as a robust second-order condition.
no code implementations • 27 May 2020 • Jakob Bossek, Pascal Kerschke, Heike Trautmann
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems.
no code implementations • 27 May 2020 • Moritz Seiler, Heike Trautmann, Pascal Kerschke
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms.
1 code implementation • 30 Mar 2020 • Jakob Bossek, Carola Doerr, Pascal Kerschke
Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter.
no code implementations • 4 Feb 2020 • Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann
In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour.
1 code implementation • 19 Dec 2019 • Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr
We study three different decision tasks: classic one-shot optimization (only the best sample matters), one-shot optimization with surrogates (allowing to use surrogate models for selecting a design that need not necessarily be one of the evaluated samples), and one-shot regression (i. e., function approximation, with minimization of mean squared error as objective).
no code implementations • 28 Nov 2018 • Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann
The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning.
no code implementations • 24 Nov 2017 • Pascal Kerschke, Heike Trautmann
In this paper, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems.
4 code implementations • 17 Aug 2017 • Pascal Kerschke
Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task.
1 code implementation • 5 Jan 2017 • Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.
2 code implementations • 8 Jun 2015 • Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Frechette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.
no code implementations • 26 Mar 2015 • Luis Marti, Christian Grimme, Pascal Kerschke, Heike Trautmann, Günter Rudolph
Therefore, we propose a postprocessing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of generated solutions after optimization in order to select a uniformly distributed subset of nondominated solutions from the archive.