Search Results for author:
Found 11 papers, 6 papers with code
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization
Reinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms.
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
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.
Simulation-guided Beam Search for Neural Combinatorial Optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
A Survey of Methods for Automated Algorithm Configuration
We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Efficient Active Search for Combinatorial Optimization Problems
While active search is simple to implement, it is not competitive with state-of-the-art methods because adjusting all model weights for each test instance is very time and memory intensive.
Learning How to Optimize Black-Box Functions With Extreme Limits on the Number of Function Evaluations
We tune this method for black box optimization and then evaluate on classical black box optimization benchmarks.
Learning a Latent Search Space for Routing Problems using Variational Autoencoders
Methods for automatically learning to solve routing problems are rapidly improving in performance.
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
Furthermore, we show for the CVRP and the SDVRP that our approach surpasses the performance of existing machine learning approaches and comes close to the performance of state-of-the-art optimization approaches.
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
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 retrieved when the port is busy.
ASlib: A Benchmark Library for Algorithm Selection
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.
