Pflacco: Feature-based landscape analysis of continuous and constrained optimization problems in python
The herein proposed Python package pflacco provides a set of numerical features to
characterize single-objective continuous and constrained optimization problems. Thereby …
characterize single-objective continuous and constrained optimization problems. Thereby …
[HTML][HTML] Tinytla: Topological landscape analysis for optimization problem classification in a limited sample setting
In numerical optimization, the characterization of optimization problems and their properties
has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization …
has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization …
A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization
The task of selecting the best optimization algorithm for a particular problem is known as
algorithm selection (AS). This involves training a model using landscape characteristics to …
algorithm selection (AS). This involves training a model using landscape characteristics to …
A collection of robotics problems for benchmarking evolutionary computation methods
The utilization of benchmarking techniques has a crucial role in the development of novel
optimization algorithms, and also in performing comparisons between already existing …
optimization algorithms, and also in performing comparisons between already existing …
Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples
The choice of input-data used to train algorithm-selection models is recognised as being a
critical part of the model success. Recently, feature-free methods for algorithm-selection that …
critical part of the model success. Recently, feature-free methods for algorithm-selection that …
How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank Algorithms?
Algorithm selection is a critical aspect of continuous black-box optimization, and various
methods have been proposed to choose the most appropriate algorithm for a given problem …
methods have been proposed to choose the most appropriate algorithm for a given problem …
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Algorithm-selection (AS) methods are essential in order to obtain the best performance from
a portfolio of solvers over large sets of instances. However, many AS methods rely on an …
a portfolio of solvers over large sets of instances. However, many AS methods rely on an …
Random Filter Map**s as Optimization Problem Feature Extractors
Characterizing optimization problems and their properties addresses a key challenge in
optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and …
optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and …
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
The selection of the most appropriate algorithm to solve a given problem instance, known as
algorithm selection, is driven by the potential to capitalize on the complementary …
algorithm selection, is driven by the potential to capitalize on the complementary …
Guest Editorial Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software
Benchmarking provides an essential ground base for adequately assessing and comparing
evolutionary computation methods and other optimization algorithms. It allows us to gain …
evolutionary computation methods and other optimization algorithms. It allows us to gain …