Benchmarking of Continuous Black Box Optimization Algorithms | Citedby Results | Evolutionary Computation | MIT Press

Skip to Main Content

Skip Nav Destination

Benchmarking of Continuous Black Box Optimization Algorithms

Evolutionary Computation (2012) 20 (4): 481.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

DE/current−to−better/1: A new mutation operator to keep population diversity

Mingcheng Zuo
Changfang Guo

Intelligent Systems with Applications (2022) 14: 200063.

Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms

Thomas Weise
Yan Chen
Xinlu Li
Zhize Wu

Applied Soft Computing (2020) 92: 106269.

Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond

Konstantinos Varelas
Ouassim Ait El Hara
Dimo Brockhoff
Nikolaus Hansen
Duc Manh Nguyen
Tea Tušar
Anne Auger

Applied Soft Computing (2020) 97: 106737.

Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness

Carlos García-Martínez
Pablo D. Gutiérrez
Daniel Molina
Manuel Lozano
Francisco Herrera

Soft Computing (2017) 21 (19): 5573.

BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology

Alejandro F Villaverde
David Henriques
Kieran Smallbone
Sophia Bongard
Joachim Schmid
Damjan Cicin-Sain
Anton Crombach
Julio Saez-Rodriguez
Klaus Mauch
Eva Balsa-Canto
Pedro Mendes
Johannes Jaeger
Julio R Banga

BMC Systems Biology (2015) 9 (1)