An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments. Olivetti de França, F., Von Zuben, F. J., & Nunes de Castro, L. In GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 289--296, New York, NY, USA, 2005. ACM.
An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments [link]Paper  doi  abstract   bibtex   
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.
@inproceedings{olivetti_de_franca_artificial_2005,
	address = {New York, NY, USA},
	title = {An {Artificial} {Immune} {Network} for {Multimodal} {Function} {Optimization} on {Dynamic} {Environments}},
	isbn = {1-59593-010-8},
	url = {http://doi.acm.org/10.1145/1068009.1068057},
	doi = {10.1145/1068009.1068057},
	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.},
	booktitle = {{GECCO} ’05: {Proceedings} of the 2005 conference on {Genetic} and evolutionary computation},
	publisher = {ACM},
	author = {Olivetti de França, Fabricio and Von Zuben, Fernando J. and Nunes de Castro, Leandro},
	year = {2005},
	pages = {289--296}
}
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