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Authors: Carlos Cotta 1 ; Antonio J. Fernández-Leiva 1 ; Francisco Fernández de Vega 2 ; Francisco Chávez 2 ; Juan J. Merelo 3 ; Pedro A. Castillo 3 ; David Camacho 4 and Gema Bello-Orgaz 4

Affiliations: 1 Universidad de Málaga, Spain ; 2 University of Extremadura, Spain ; 3 Universidad de Granada, Spain ; 4 Universidad Autónoma de Madrid, Spain

ISBN: 978-989-758-157-1

Keyword(s): Ephemeral Computing, Bioinspired Optimization, Evolutionary Computation, Complex Systems, Autonomic Computing, Distributed Computing.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biocomputing and Complex Adaptive Systems ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts, mainly in the presence of devices “lent” voluntarily by their users. A highly dynamic and volatile computational landscape emerges from the collective contribution of numerous such devices. Algorithms consciously running on these environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing self-adaptation capabilities to these techniques, yet the science of self-? bionspired algorithms is still nascent, in particular regarding to higher-level self-adaptation, and self-management in the context of large scale optimization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on this scenario will also pave the way for the application of other techniques on this computational domain. (More)

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Paper citation in several formats:
Cotta, C.; Fernández-Leiva, A.; Fernández de Vega, F.; Chávez, F.; Merelo, J.; Castillo, P.; Camacho, D. and Bello-Orgaz, G. (2015). Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 319-324. DOI: 10.5220/0005628903190324

@conference{ecta15,
author={Carlos Cotta. and Antonio J. Fernández{-}Leiva. and Francisco Fernández de Vega. and Francisco Chávez. and Juan J. Merelo. and Pedro A. Castillo. and David Camacho. and Gema Bello{-}Orgaz.},
title={Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={319-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005628903190324},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities
SN - 978-989-758-157-1
AU - Cotta, C.
AU - Fernández-Leiva, A.
AU - Fernández de Vega, F.
AU - Chávez, F.
AU - Merelo, J.
AU - Castillo, P.
AU - Camacho, D.
AU - Bello-Orgaz, G.
PY - 2015
SP - 319
EP - 324
DO - 10.5220/0005628903190324

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