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
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 o
n 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)