Search Results for author: Pascal Kerschke

Found 24 papers, 11 papers with code

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization

no code implementations11 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.

Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games

no code implementations16 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.

Evolutionary Algorithms

Mixture of Decision Trees for Interpretable Machine Learning

1 code implementation26 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).

Interpretable Machine Learning

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

1 code implementation30 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.

Hyperparameter Optimization

To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes

1 code implementation29 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.

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem

1 code implementation29 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).

feature selection

Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems

no code implementations25 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.

One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes

1 code implementation20 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.

Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries

no code implementations27 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.

Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection

no code implementations27 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.

Benchmarking Combinatorial Optimization

Initial Design Strategies and their Effects on Sequential Model-Based Optimization

1 code implementation30 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.

The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics

no code implementations4 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.

One-Shot Decision-Making with and without Surrogates

1 code implementation19 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).

Decision Making regression

Automated Algorithm Selection: Survey and Perspectives

no code implementations28 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.


Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning

no code implementations24 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.

BIG-bench Machine Learning

Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco

4 code implementations17 Aug 2017 Pascal Kerschke

Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task.

ASlib: A Benchmark Library for Algorithm Selection

2 code implementations8 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.

Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms

no code implementations26 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.

Multiobjective Optimization

Cannot find the paper you are looking for? You can Submit a new open access paper.