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Found 24 papers, 11 papers with code
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite.
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.
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.
Mixture of Decision Trees for Interpretable Machine Learning
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT).
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.
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
It is able to efficiently model and exploit LE sets in MMMOO 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.
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
Simultaneously visualizing the decision and objective space of continuous multi-objective optimization problems (MOPs) recently provided key contributions in understanding the structure of their landscapes.
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.
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.
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).
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization.
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
Therefore, we build on the GFH approach but apply a new technique for approximating the location of locally efficient points and using the divergence of the multi-objective gradient vector field as a robust second-order condition.
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.
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.
Initial Design Strategies and their Effects on Sequential Model-Based Optimization
Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter.
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour.
One-Shot Decision-Making with and without Surrogates
We study three different decision tasks: classic one-shot optimization (only the best sample matters), one-shot optimization with surrogates (allowing to use surrogate models for selecting a design that need not necessarily be one of the evaluated samples), and one-shot regression (i. e., function approximation, with minimization of mean squared error as objective).
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.
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 optimization algorithms is usually a difficult and complex task.
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.
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.
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.
