[HTML][HTML] Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
Landscape-aware performance prediction for evolutionary multiobjective optimization
We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …
Automated algorithm selection: Survey and perspectives
It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …
intensely studied, different instances are best solved using different algorithms. This is …
[HTML][HTML] Algorithm runtime prediction: Methods & evaluation
Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …
previously unseen input, using machine learning techniques to build a model of the …
Benchmarking in optimization: Best practice and open issues
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 …
different backgrounds and from different institutes around the world. Promoting best practice …
Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
P Kerschke, H Trautmann - Evolutionary computation, 2019 - direct.mit.edu
In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
[HTML][HTML] Aslib: A benchmark library for algorithm selection
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 of …
per-instance basis in order to exploit the varying performance of algorithms over a set of …
Comprehensive feature-based landscape analysis of continuous and constrained optimization problems using the R-package flacco
P Kerschke, H Trautmann - … in Statistical Computing: From Music Data …, 2019 - Springer
Choosing the best-performing optimizer (s) out of a portfolio of optimization algorithms is
usually a difficult and complex task. It gets even worse, if the underlying functions are …
usually a difficult and complex task. It gets even worse, if the underlying functions are …
Improving the state-of-the-art in the traveling salesman problem: An anytime automatic algorithm selection
II Huerta, DA Neira, DA Ortega, V Varas… - Expert Systems with …, 2022 - Elsevier
This work presents a new metaheuristic for the euclidean Traveling Salesman Problem
(TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five …
(TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five …
Leveraging TSP solver complementarity through machine learning
Abstract The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have been …
problems. Over the years, many different solution approaches and solvers have been …