activity
20142024
most citedInfeasibility and structural bias in Differential Evolution

104 citations · 366 across the 10 of their papers we have counts for

collaborators
Showing cs.NEShow all

6 papers · 1 filter

cs.NE2024★ 19 cited

An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms

Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman +1

Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization pr…

cs.NE2021★ 15 cited

Emergence of Structural Bias in Differential Evolution

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. The complexity of these problems is wel…

cs.NE2020★ 37 cited

Differential evolution outside the box

Anna V. Kononova, Fabio Caraffini, Thomas Bäck

This paper investigates how often the popular configurations of Differential Evolution generate solutions outside the feasible domain. Following previous publications in the field,…

cs.NE2019★ 104 cited

Infeasibility and structural bias in Differential Evolution

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 th…

cs.NE2018★ 69 cited

Multi-Strategy Coevolving Aging Particle Optimization

Giovanni Iacca, Fabio Caraffini, Ferrante Neri

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components w…

cs.NE2014★ 93 cited

Structural bias in population-based algorithms

Anna V. Kononova, David W. Corne, Philippe De Wilde +2

Challenging optimisation problems are abundant in all areas of science. Since the 1950s, scientists have developed ever-diversifying families of black box optimisation algorithms d…