9 citations · 14 across the 11 of their papers we have counts for
18 papers
Mixture of Decision Trees for Interpretable Machine Learning
Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke +1
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble archite…
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
Recent advances in the visualization of continuous multimodal multi-objective optimization (MMMOO) landscapes brought a new perspective to their search dynamics. Locally efficient…
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke +1
Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucia…
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
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 stru…
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Vera Steinhoff, Pascal Kerschke, Pelin Aspar +2
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategie…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…