Article

An artificial immune network for multimodal function optimization on dynamic environments

Authors:

Fabricio Olivetti de França,

Fernando J. Von Zuben,

Leandro Nunes de CastroAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 289 - 296

https://doi.org/10.1145/1068009.1068057

Published: 25 June 2005 Publication History

Abstract

Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature.

References

[1]

Allen, D. et al., Timing, Genetic Requirements and Functional Consequences of Somatic Hypermutation During B-cell Development, Imm. Rev., 96, pp. 5--22, 1987.

[2]

Angeline, P. J. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences, in Proc. Evol. Prog. VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Germany: Springer-Verlag, 1998, pp. 601--610.

Digital Library

[3]

Angeline, P. J. Tracking extrema in dynamic environments. In Proceedings of the 6th International Conference on Evolutionary Programming (Indianapolis,Indiana, USA, Apr. 13-16 1997), P. J. Angeline, R. G. Reynolds, J. R. McDonnell, and R. C. Eberhart, Eds., vol. 1213 of Lecture Notes in Computer Science,Springer Verlag.

Digital Library

[4]

Bazaraa, M. S., Sherali, H. D., and Shetty, C. M., Nonlinear Programming: Theory and Algorithms, 2nd Ed., Wiley, 1993.

[5]

de Castro, L. N. & Von Zuben, F. J., Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), pp. 239--251, 2002.

Digital Library

[6]

de Castro, L. N. and Timmis, J. Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag.

Digital Library

[7]

de Castro, L. N. and Timmis, J. An Artificial Immune Network for Multimodal Function Optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC'02), Vol. 1, pp. 699--674, May, Hawaii, 2002.

[8]

Gaspar, A., Collard, P. From GAs to Artificial Immune Systems: Improving Adaptation in Time Dependent Optimization. Proceedings of the Congress on Evolutionary Computation. pp. 1859--1866, Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzal (Eds).

[9]

Holland, P. W. H., Garcia-Fernandez, J., Williams, N. A. and Sidow, A. (1994). Gene duplications and origins of vertebrate development. Development Supplement, pp. 125--133.

[10]

Kelsey, J., Timmis, J., Hone, A. Chasing Chaos. In R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D. Essam, and T. Gedeon, editors. Proceedings of the Congress on Evolutionary Computation, pp. 413--419, Canberra. Australia, December. IEEE.

[11]

Leung, Y. and Wang, Y. An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization, IEEE Trans. Evol. Comput. Vol. 5, No. 1, pp. 41--53, 2001.

Digital Library

[12]

Ohno, S. Evolution by Gene Duplication. Allen and Unwin, London, 1970

[13]

Perelson, A. S., Immune Network Theory, Imm. Rev., 110, pp. 5--36, 1989.

[14]

Siarry, P., Berthiau, G., Durbin, F. and Haussy, J. Enhanced simulated annealing for globally minimizing functions of many-continuous variables. Neural Networks, vol. 3, pp. 467--483, 1990.

[15]

Timmis, J. and Edmonds, C., A Comment on opt-AINet: An Immune Network Algorithm for Optimisation, In D. Kalyanmoy et al, editor, Genetic and Evolutionary Computation, volume 3102 of Lecture Notes in Computer Science, pp. 308--317, Springer, 2004.

[16]

Timmis, J., Edmonds, C., and Kelsey, J. Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation, In Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 1044--1051, 2004.

[17]

Walker, J., Garrett, S. Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolutionary Strategies. Lecture Notes in Computer Science 2787, pp. 273--284. Timmis, J., Bentley, P. and Hart, E.(Eds).

[18]

Yao, X. and Liu, Y. Fast evolution strategies, in Evolutionary Programming VI, P. J. Angeline, R. Reynolds, J. McDonnell, and R. Eberhart, Eds. Berlin, Germany: Springer-Verlag, 1997, pp. 151--161.

Digital Library

Cited By

Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
https://doi.org/10.3390/a17010018
Li LLin QMing Z(2022)A survey of artificial immune algorithms for multi-objective optimizationNeurocomputing10.1016/j.neucom.2021.08.154489(211-229)Online publication date: Jun-2022
https://doi.org/10.1016/j.neucom.2021.08.154
Etaati BGhorrati ZEbadzadeh M(2022)A full-featured cooperative coevolutionary memory-based artificial immune system for dynamic optimizationApplied Soft Computing10.1016/j.asoc.2021.108389117:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.asoc.2021.108389
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Index Terms

An artificial immune network for multimodal function optimization on dynamic environments
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
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General Chair:
Una-May O'Reilly
CSAIL, MIT

Copyright © 2005 ACM.

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Published: 25 June 2005

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June 25 - 29, 2005

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Rezvanian AVahidipour SSaghiri A(2023)CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic EnvironmentsAlgorithms10.3390/a1701001817:1(18)Online publication date: 30-Dec-2023
https://doi.org/10.3390/a17010018
Li LLin QMing Z(2022)A survey of artificial immune algorithms for multi-objective optimizationNeurocomputing10.1016/j.neucom.2021.08.154489(211-229)Online publication date: Jun-2022
https://doi.org/10.1016/j.neucom.2021.08.154
Etaati BGhorrati ZEbadzadeh M(2022)A full-featured cooperative coevolutionary memory-based artificial immune system for dynamic optimizationApplied Soft Computing10.1016/j.asoc.2021.108389117:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.asoc.2021.108389
Kanwal SHussain AHuang K(2020)Novel Artificial Immune Networks-based Optimization of Shallow Machine Learning (ML) ClassifiersExpert Systems with Applications10.1016/j.eswa.2020.113834(113834)Online publication date: Aug-2020
https://doi.org/10.1016/j.eswa.2020.113834
Shatnawi YQuwaider M(2019)Congestion Control in ATM networks using PID Controller with Immune Algorithm2019 10th International Conference on Information and Communication Systems (ICICS)10.1109/IACS.2019.8809150(19-24)Online publication date: Jun-2019
https://doi.org/10.1109/IACS.2019.8809150
Ianigro SBown O(2019)Exploring Transfer Functions in Evolved CTRNNs for Music GenerationComputational Intelligence in Music, Sound, Art and Design10.1007/978-3-030-16667-0_16(234-248)Online publication date: 27-Mar-2019
https://doi.org/10.1007/978-3-030-16667-0_16
Bennour ALaouar MKajan E(2018)Clonal selection classification algorithm applied to arabic writer identificationProceedings of the 8th International Conference on Information Systems and Technologies10.1145/3200842.3208087(1-5)Online publication date: 16-Mar-2018
https://dl.acm.org/doi/10.1145/3200842.3208087
Haghbayan PNezamabadi-pour HKamyab S(2017)A niche GSA method with nearest neighbor scheme for multimodal optimizationSwarm and Evolutionary Computation10.1016/j.swevo.2017.03.00235(78-92)Online publication date: Aug-2017
https://doi.org/10.1016/j.swevo.2017.03.002
Sun WLin AYu HLiang QWu G(2017)All-dimension neighborhood based particle swarm optimization with randomly selected neighborsInformation Sciences: an International Journal10.1016/j.ins.2017.04.007405:C(141-156)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.04.007
Núñez JCabido RMontemayor APantrigo J(2017)Real-time human body tracking based on data fusion from multiple RGB-D sensorsMultimedia Tools and Applications10.1007/s11042-016-3759-676:3(4249-4271)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s11042-016-3759-6
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