BIAS: a toolbox for benchmarking structural bias in the continuous domain

Algorithms and the Foundations of Software technolog

Large Scale Problems in Practice: The effect of dimensionality on the interaction among variables

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article performs a study on correlation between pairs of variables in dependence on the problem dimensionality. Two tests, based on Pearson and Spearman coefficients, have been designed and used in this work. In total, $86$ test problems ranging between 10 and 1000 variables have been studied. If the most commonly used experimental conditions are used, the correlation between pairs of variables appears, from the perspective of the search algorithm, to consistently decrease. This effect is not due to the fact that the dimensionality modifies the nature of the problem but is a consequence of the experimental conditions: the computational feasibility of the experiments imposes an extremely shallow search in case of high dimensions. An exponential increase of budget and population with the dimensionality is still practically impossible. Nonetheless, since real-world application may require that large scale problems are tackled despite of the limited budget, an algorithm can quickly improve upon initial guesses if it integrates the knowledge that an apparent weak correlation between pairs of variables occurs, regardless the nature of the problem

Using Structural Bias to Analyse the Behaviour of Modular CMA-ES

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whether CMAES suffers from structural bias and which modules and parameters affect the strength and type of structural bias. Structural bias occurs when an algorithm or a component of the algorithm biases the search towards a specific direction in the search space irrespective of the objective function. In addition to this investigation, we propose a method to assess the relationship between structural bias and the performance of configurations with different types of bias on the BBOB suite of benchmark functions. Surprisingly for such a popular algorithm, 90.3% of the 1 620 CMA-ES configurations were found to have Structural Bias. Some interesting patterns between module settings and bias types are presented and further insights are discussed

Full Geant4 and FLUKA simulations of an e-LINAC for its use in particle detectors performance tests

In this work we present the results of full Geant4 and FLUKA simulations and comparison with dosimetry data of an electron LINAC of St. Maria Hospital located in Terni, Italy. The facility is being used primarily for radiotherapy and the goal of present study is the detailed investigation of electron beam parameters to evaluate the possibility to use e-LINAC (during time slots when it is not used for radiotherapy) to test the performance of detector systems in particular those designed to operate in space. The critical beam parameters are electron energy, profile and flux available at the surface of device to be tested. The present work aims to extract these parameters from dosimetry calibration data available at e-LINAC. The electron energy range is from 4 MeV to 20 MeV. The dose measurements have been performed by using an Advanced Markus Chamber which has a small sensitive volume