Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges
Selecting the most appropriate algorithm to use when attempting to solve a black-box
continuous optimization problem is a challenging task. Such problems typically lack …
continuous optimization problem is a challenging task. Such problems typically lack …
Exploratory landscape analysis of continuous space optimization problems using information content
Data-driven analysis methods, such as the information content of a fitness sequence,
characterize a discrete fitness landscape by quantifying its smoothness, ruggedness, or …
characterize a discrete fitness landscape by quantifying its smoothness, ruggedness, or …
Performance analysis of continuous black-box optimization algorithms via footprints in instance space
MA Muñoz, KA Smith-Miles - Evolutionary computation, 2017 - ieeexplore.ieee.org
This article presents a method for the objective assessment of an algorithm's strengths and
weaknesses. Instead of examining the performance of only one or more algorithms on a …
weaknesses. Instead of examining the performance of only one or more algorithms on a …
Generating new space-filling test instances for continuous black-box optimization
MA Muñoz, K Smith-Miles - Evolutionary computation, 2020 - direct.mit.edu
This article presents a method to generate diverse and challenging new test instances for
continuous black-box optimization. Each instance is represented as a feature vector of …
continuous black-box optimization. Each instance is represented as a feature vector of …
A meta-learning prediction model of algorithm performance for continuous optimization problems
Algorithm selection and configuration is a challenging problem in the continuous
optimization domain. An approach to tackle this problem is to develop a model that links …
optimization domain. An approach to tackle this problem is to develop a model that links …
An instance space analysis of regression problems
MA Muñoz, T Yan, MR Leal, K Smith-Miles… - ACM Transactions on …, 2021 - dl.acm.org
The quest for greater insights into algorithm strengths and weaknesses, as revealed when
studying algorithm performance on large collections of test problems, is supported by …
studying algorithm performance on large collections of test problems, is supported by …
Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation
Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is
imperative to evaluate the predictive performance it attains for diverse datasets. This is done …
imperative to evaluate the predictive performance it attains for diverse datasets. This is done …
Analyzing randomness effects on the reliability of exploratory landscape analysis
The inherent difficulty of solving a continuous, static, bound-constrained and single-objective
black-box optimization problem depends on the characteristics of the problem's fitness …
black-box optimization problem depends on the characteristics of the problem's fitness …
Tuning metaheuristics by sequential optimisation of regression models
ÁR Trindade, F Campelo - Applied Soft Computing, 2019 - Elsevier
Tuning parameters is an important step for the application of metaheuristics to specific
problem classes. In this work we present a tuning framework based on the sequential …
problem classes. In this work we present a tuning framework based on the sequential …
Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis
Protein structure prediction (PSP) from its primary sequence is a challenging task in
computational biology. PSP is an optimization problem that determines the stable or native …
computational biology. PSP is an optimization problem that determines the stable or native …