Article

A population adaptive based immune algorithm for solving multi-objective optimization problems

Authors:

Mahdi MahfoufAuthors Info & Claims

ICARIS'06: Proceedings of the 5th international conference on Artificial Immune Systems

Pages 280 - 293

https://doi.org/10.1007/11823940_22

Published: 04 September 2006 Publication History

Abstract

The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.

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Cited By

Zhang MArcuri A(2021)Adaptive Hypermutation for Search-Based System Test Generation: A Study on REST APIs with EvoMasterACM Transactions on Software Engineering and Methodology10.1145/346494031:1(1-52)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3464940
Ahn CKim EKim HLim DAn J(2010)A hybrid multiobjective evolutionary algorithmMathematical and Computer Modelling: An International Journal10.1016/j.mcm.2010.06.00752:11-12(2048-2059)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1016/j.mcm.2010.06.007

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Information

Published In

cover image Guide Proceedings

ICARIS'06: Proceedings of the 5th international conference on Artificial Immune Systems

September 2006

458 pages

ISBN:3540377492

Editors:
Hugues Bersini
IRIDIA, ULB, Bruxelles, Belgium
,
Jorge Carneiro
Instituto Gulbenkian de Ciencia, Oeiras, Portugal

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 04 September 2006

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Cited By

Zhang MArcuri A(2021)Adaptive Hypermutation for Search-Based System Test Generation: A Study on REST APIs with EvoMasterACM Transactions on Software Engineering and Methodology10.1145/346494031:1(1-52)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3464940
Ahn CKim EKim HLim DAn J(2010)A hybrid multiobjective evolutionary algorithmMathematical and Computer Modelling: An International Journal10.1016/j.mcm.2010.06.00752:11-12(2048-2059)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1016/j.mcm.2010.06.007

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