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Found 15 papers, 2 papers with code
Exploratory Landscape Analysis for Mixed-Variable Problems
We provide a comprehensive juxtaposition of the results based on these different techniques.
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems
As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA.
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems.
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
The quality of our approaches is on par with methods relying on the traditional landscape features.
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress.
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 Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS).
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras).
Dynamic Bi-Objective Routing of Multiple Vehicles
In practice, e. g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests.
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms.
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems.
Automated Algorithm Selection: Survey and Perspectives
The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning.
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 efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems.
An Ontology of Preference-Based Multiobjective Metaheuristics
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization.
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms
Therefore, we propose a postprocessing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of generated solutions after optimization in order to select a uniformly distributed subset of nondominated solutions from the archive.
