122 citations · 279 across the 10 of their papers we have counts for
10 papers
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Viktor Bengs +3
We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Recently, there ha…
Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions
Jakob Pfrommer, Anne Meyer, Kevin Tierney
Block stacking storage systems are highly adaptable warehouse systems with low investment costs. With multiple, deep lanes they can achieve high storage densities, but accessing so…
Simulation-guided Beam Search for Neural Combinatorial Optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim +4
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches ca…
A Survey of Methods for Automated Algorithm Configuration
Elias Schede, Jasmin Brandt, Alexander Tornede +4
Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of A…
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
Efficient Active Search for Combinatorial Optimization Problems
André Hottung, Yeong-Dae Kwon, Kevin Tierney
Recently numerous machine learning based methods for combinatorial optimization problems have been proposed that learn to construct solutions in a sequential decision process via r…
Learning How to Optimize Black-Box Functions With Extreme Limits on the Number of Function Evaluations
Carlos Ansotegui, Meinolf Sellmann, Tapan Shah +1
We consider black-box optimization in which only an extremely limited number of function evaluations, on the order of around 100, are affordable and the function evaluations must b…
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
André Hottung, Kevin Tierney
Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuris…
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
André Hottung, Shunji Tanaka, Kevin Tierney
The container pre-marshalling problem (CPMP) is concerned with the re-ordering of containers in container terminals during off-peak times so that containers can be quickly retrieve…
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff +8
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set…