International Journal of Computational Intelligence Systems

Volume 12, Issue 1, November 2018, Pages 13 - 27

Germinal Center Optimization Algorithm

Authors
Carlos Villaseñor1, *, Nancy Arana-Daniel1, *, Alma Y. Alanis1, Carlos López-Franco1, Esteban A. Hernandez-Vargas2
1Departamento de Computación, Universidad de Guadalajara, Blvd. Marcelino Garca Barragn 1421, 44430, Guadalajara, Jalisco, México
2Frankfurt Institute for Advance Studies, Ruth-Moufang-Strae 1, 60438, Frankfurt am Main, Germany
Corresponding Author
Carlos Villaseñor
Received 7 December 2017, Accepted 3 August 2018, Available Online 1 November 2018.
DOI
https://doi.org/10.2991/ijcis.2018.25905179How to use a DOI?
Keywords
Germinal Center, Artificial Immune Systems, Evolutionary Optimization
Abstract

Artificial immune systems are metaheuristic algorithms that mimic the adaptive capabilities of the immune system of vertebrates. Since the 1990s, they have become one of the main branches of computer intelligence. However, there are still many competitive processes in the biological phenomena that can bring new advances for many applications. The Germinal Center reaction is one of these competitive processes that had not been fully modeled until now, and that was the inspiration to design the novel optimization algorithm that we present in this work. Our proposal implements a competitive-based nonuniform distribution to select particles to be mutated, which can be interpreted as an implementation of temporal leadership in population-based metaheuristics. We model the dark-zone and light-zone of the Germinal Center and their competitive processes like clonal expansion, T-cell binding and life signal decay. We also propose the combination of this selection method with the use of one Differential Evolution-based strategy to substitute the somatic hypermutation process. To show the performance, we include a benchmark with the comparison of our approach versus some of the state-of-the-art bio-inspired optimization algorithms. We show that the proposal has a statistically significant improvement over the other algorithms for low dimensionality problems.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 1
Pages
13 - 27
Publication Date
2018/11
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2018.25905179How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Carlos Villaseñor
AU  - Nancy Arana-Daniel
AU  - Alma Y. Alanis
AU  - Carlos López-Franco
AU  - Esteban A. Hernandez-Vargas
PY  - 2018
DA  - 2018/11
TI  - Germinal Center Optimization Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 13
EP  - 27
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905179
DO  - https://doi.org/10.2991/ijcis.2018.25905179
ID  - Villaseñor2018
ER  -