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02/16/2023
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
The term Procedural Content Generation (PCG) refers to the (semi-)automa...
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11/26/2022
Mixture of Decision Trees for Interpretable Machine Learning
This work introduces a novel interpretable machine learning method calle...
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07/30/2022
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Hyperparameter optimization (HPO) is a key component of machine learning...
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04/22/2022
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
Recent advances in the visualization of continuous multimodal multi-obje...
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04/12/2022
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
Exploratory Landscape Analysis is a powerful technique for numerically c...
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11/29/2020
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
Simultaneously visualizing the decision and objective space of continuou...
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10/02/2020
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Multimodality is one of the biggest difficulties for optimization as loc...
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07/07/2020
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen re...
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06/29/2020
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
In this work we focus on the well-known Euclidean Traveling Salesperson ...
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06/25/2020
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
When dealing with continuous single-objective problems, multimodality po...
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06/20/2020
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
Visualization techniques for the decision space of continuous multi-obje...
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05/27/2020
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
Artificial neural networks in general and deep learning networks in part...
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05/27/2020
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-know...
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03/30/2020
Initial Design Strategies and their Effects on Sequential Model-Based Optimization
Sequential model-based optimization (SMBO) approaches are algorithms for...
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02/04/2020
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
Several important optimization problems in the area of vehicle routing c...
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12/19/2019
One-Shot Decision-Making with and without Surrogates
One-shot decision making is required in situations in which we can evalu...
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11/28/2018
Automated Algorithm Selection: Survey and Perspectives
It has long been observed that for practically any computational problem...
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06/20/2018
Estimation of component reliability in repairable series systems with masked cause of failure by means of latent variables
In this work, we propose two methods, a Bayesian and a maximum likelihoo...
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11/24/2017
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
In this paper, we build upon previous work on designing informative and ...
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08/17/2017
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
Choosing the best-performing optimizer(s) out of a portfolio of optimiza...
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01/05/2017
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
OpenML is an online machine learning platform where researchers can easi...
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06/08/2015
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a se...
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03/26/2015
