1 code implementation • 5 Aug 2024 • Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman, Giovanni Iacca
Hyperparameter optimization is a crucial problem in Evolutionary Computation.
no code implementations • 13 Apr 2024 • Juan F. Pérez-Pérez, Pablo Isaza Gómez, Isis Bonet, María Solange Sánchez-Pinzón, Fabio Caraffini, Christian Lochmuller
These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study.
no code implementations • 20 Feb 2024 • Adebamigbe Fasanmade, Ali H. Al-Bayatti, Jarrad Neil Morden, Fabio Caraffini
We also study the correlation between distraction (driver, vehicle, and environment) and the classification severity based on a continuous distraction severity score.
no code implementations • 20 May 2023 • Mădălina-Andreea Mitran, Anna V. Kononova, Fabio Caraffini, Daniela Zaharie
This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated.
no code implementations • 19 Apr 2023 • Diederick Vermetten, Fabio Caraffini, Anna V. Kononova, Thomas Bäck
Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.
1 code implementation • 4 Apr 2023 • Bas van Stein, Diederick Vermetten, Fabio Caraffini, Anna V. Kononova
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
1 code implementation • 6 Nov 2022 • Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly, Fabio Caraffini, Ricardo Moreira Borges
This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation.
1 code implementation • 7 Mar 2022 • Anna V. Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A. Mitran, Daniela Zaharie
Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity.
no code implementations • 10 May 2021 • Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
no code implementations • 10 May 2021 • Bas van Stein, Fabio Caraffini, Anna V. Kononova
Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.
no code implementations • 22 Apr 2020 • Anna V. Kononova, Fabio Caraffini, Thomas Bäck
A wide range of popular Differential Evolution configurations is considered in this study.
no code implementations • 11 Apr 2020 • Johana Florez-Lozano, Fabio Caraffini, Carlos Parra, Mario Gongora
Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making.
no code implementations • 18 Jan 2019 • Fabio Caraffini, Anna V. Kononova, David Corne
This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values.
no code implementations • 11 Oct 2018 • Giovanni Iacca, Fabio Caraffini, Ferrante Neri
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization.
no code implementations • 12 Sep 2018 • Giovanni Iacca, Fabio Caraffini
The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions.
no code implementations • 22 Aug 2014 • Anna V. Kononova, David W. Corne, Philippe De Wilde, Vsevolod Shneer, Fabio Caraffini
Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.