Add to ORKGInternational audienceBesides the conventional and established applications of sensitivity analysis in the field of optimization, sensitivity analysis-based approaches can also be used in research on systematic performance analysis of meta-heuristic optimization algorithms, and the construction of synthetic industrially relevant benchmark problems. By means of academic examples as well as industrial case studies, we demonstrate innovative approaches which set steps towards systematic and representative benchmarking and empirical optimization algorithm performance analysis
A pervasive problem in the field of optimization algorithms is the lack of meaningful and consistent...
Benchmarks are important for comparing performance of optimisation algorithms, but we can select ins...
The framework for sensitivity analysis in discrete multi-criteria decision analysis developed by Rio...
International audienceBesides the conventional and established applications of sensitivity analysis ...
L'optimisation difficile représente une classe de problèmes dont la résolution ne peut être obtenue ...
Research on new optimization algorithms is often funded based on the motivation that such algorithms...
The development of algorithms for tackling continuous optimization problems has been one of the most...
Reliable comparison of optimization algorithms requires the use of specialized benchmarking procedur...
Modern data-driven statistical techniques, e.g., non-linear classification and regression machine ...
International audienceIn the last decade, many new algorithms have been proposed to solve optimizati...
Benchmarks are important for comparing performance of optimisation algorithms, but we can select ins...
ABSTRACT: The authors got the motivation for writing the article based on an issue, with which devel...
In Operations Research, sensitivity analysis describes the methods and tools used to study how the o...
Hard optimization stands for a class of problems which solutions cannot be found by an exact method,...
While mimicking a physical phenomenon in a computational framework, there are tuning parameters quit...
A pervasive problem in the field of optimization algorithms is the lack of meaningful and consistent...
Benchmarks are important for comparing performance of optimisation algorithms, but we can select ins...
The framework for sensitivity analysis in discrete multi-criteria decision analysis developed by Rio...
International audienceBesides the conventional and established applications of sensitivity analysis ...
L'optimisation difficile représente une classe de problèmes dont la résolution ne peut être obtenue ...
Research on new optimization algorithms is often funded based on the motivation that such algorithms...
The development of algorithms for tackling continuous optimization problems has been one of the most...
Reliable comparison of optimization algorithms requires the use of specialized benchmarking procedur...
Modern data-driven statistical techniques, e.g., non-linear classification and regression machine ...
International audienceIn the last decade, many new algorithms have been proposed to solve optimizati...
Benchmarks are important for comparing performance of optimisation algorithms, but we can select ins...
ABSTRACT: The authors got the motivation for writing the article based on an issue, with which devel...
In Operations Research, sensitivity analysis describes the methods and tools used to study how the o...
Hard optimization stands for a class of problems which solutions cannot be found by an exact method,...
While mimicking a physical phenomenon in a computational framework, there are tuning parameters quit...
A pervasive problem in the field of optimization algorithms is the lack of meaningful and consistent...
Benchmarks are important for comparing performance of optimisation algorithms, but we can select ins...
The framework for sensitivity analysis in discrete multi-criteria decision analysis developed by Rio...