An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to be executed in the same machine if they are to use the same resources. Unfortunately, the implementation code of the algorithms ...
Chromosomal instability (CIN) is a driver of cancer metastasis1-4, yet the extent to which this effect depends on the immune system remains unknown. Using ContactTracing-a newly developed, validated and benchmarked tool to infer the nature and conditional dependence of cell-cell interactions from single-cell transcriptomic data-we show that CIN-ind...
Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review...
Published in E3S Web of Conferences
The relevant literature on environmental strategy has carried out extensive research on the internal and external factors that affect the sustainable development of enterprises, but some important horizontal factors, such as the influence from peers, are ignored. Based on benchmarking theory and expectation theory, this paper makes an empirical ana...
We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone ...
BACKGROUND: Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery. METHODS: This retrospective cohort study used Hospital Episode Statistics (HES) dat...
Comparing and evaluating optimization algorithms is an important part of evolutionary computation, and requires a robust benchmarking setup to be done well. IOHprofiler supports researchers in this task by providing an easy-to-use, interactive, and highly customizable environment for benchmarking iterative optimizers.IOHprofiler is designed as a mo...
Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate and therefore allow only small evaluation budgets. Regardless of the size of the budget, high dimensionality also poses a challenge to BO, whose performance reportedly often suffers when the dimension ...
Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects the insights that can be gained from any given benchmark study. One way to easily extend the range of available benchmark functions is through affine combinations between...
In this paper we introduce modified versions of CMA-ES with the objective to help to prove convergence of CMA-ES. In order to ensure that the modifications do not alter the performances of the algorithm too much, we benchmark variants of the algorithm derived from them on problems of the bbob test suite. We observe that the main performances losses...
