Search Results for author: Fabio Caraffini

Found 9 papers, 1 papers with code

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

1 code implementation7 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.

Emergence of Structural Bias in Differential Evolution

no code implementations10 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.

Is there Anisotropy in Structural Bias?

no code implementations10 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.

Differential evolution outside the box

no code implementations22 Apr 2020 Anna V. Kononova, Fabio Caraffini, Thomas Bäck

A wide range of popular Differential Evolution configurations is considered in this study.

Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform

no code implementations11 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.

Decision Making Landmine

Infeasibility and structural bias in Differential Evolution

no code implementations18 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.

Multi-Strategy Coevolving Aging Particle Optimization

no code implementations11 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.

Compact Optimization Algorithms with Re-sampled Inheritance

no code implementations12 Sep 2018 Giovanni Iacca, Fabio Caraffini

The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions.

Structural bias in population-based algorithms

no code implementations22 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.

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