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[HTML][HTML] A prescription of methodological guidelines for comparing bio-inspired optimization algorithms
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …
growing research topic with many competitive bio-inspired algorithms being proposed every …
Anytime performance assessment in blackbox optimization benchmarking
We present concepts and recipes for the anytime performance assessment when
benchmarking optimization algorithms in a blackbox scenario. We consider runtime …
benchmarking optimization algorithms in a blackbox scenario. We consider runtime …
Revisiting Bayesian optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for
optimizing numerically costly functions. However, BO is not often compared to widely …
optimizing numerically costly functions. However, BO is not often compared to widely …
Deep-ela: Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems
In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to
numerically characterize single-objective continuous optimization problems has been …
numerically characterize single-objective continuous optimization problems has been …
High dimensional Bayesian optimization assisted by principal component analysis
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has
been successfully applied in various fields, eg, automated machine learning and design …
been successfully applied in various fields, eg, automated machine learning and design …
Expected improvement versus predicted value in surrogate-based optimization
Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to
decide which point to evaluate next. When Kriging is used as the surrogate model of choice …
decide which point to evaluate next. When Kriging is used as the surrogate model of choice …
Handling bound constraints in CMA-ES: An experimental study
R Biedrzycki - Swarm and Evolutionary Computation, 2020 - Elsevier
Bound constraints are the lower and upper limits defined for each coordinate of the solution.
There are many methods to deal with them, but there is no clear guideline for which of them …
There are many methods to deal with them, but there is no clear guideline for which of them …
Self-Adapting Particle Swarm Optimization for continuous black box optimization
This paper introduces a new version of a hyper-heuristic framework: Generalized Self-
Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is …
Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is …
[HTML][HTML] Benchmarking surrogate-based optimisation algorithms on expensive black-box functions
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box
optimisation problems with expensive objectives, such as hyperparameter tuning or …
optimisation problems with expensive objectives, such as hyperparameter tuning or …
Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
In many practical applications, usually, similar optimisation problems or scenarios
repeatedly appear. Learning from previous problem-solving experiences can help adjust …
repeatedly appear. Learning from previous problem-solving experiences can help adjust …