Paper Info
Title
Bioinspired algorithms and complex systems
Abstract
Bioinspired algorithms are search, optimization, and learning techniques whose functioning is based on some metaphor of a biological process. Prominent examples include evolutionary algorithms and swarm intelligence methods. The practical application of these techniques to real-world problems typically involves orchestrating the interplay among different algorithmic components in order to attain synergistic search capabilities. This is a common theme in complex systems, in which the whole is more than the sum of the parts due to the complex interaction patterns among system components, giving rise to emergent properties not anticipated at the base level. Indeed, such systems are prevalent in many contexts, both natural (biological systems, ecosystems, etc.) and artificial (social networks, finance markets, etc.). Analyzing and understanding such systems is not only of the foremost interest, but also constitutes in general a formidable task requiring powerful tools. Metaheuristics in general and bioinspired algorithms in particular can greatly contribute to this end. Furthermore, their intrinsic complex nature makes them prone to be also subject of analysis using a complex-system perspective.
Year
DOI
Venue
2017
10.1016/j.jocs.2017.11.010
Journal of Computational Science
Keywords
Field
DocType
Complex systems,Bioinspired algorithms,Metaheuristics
Complex system,Social network,Evolutionary algorithm,Computer science,Swarm intelligence,Algorithm,Theoretical computer science,Instrumental and intrinsic value,Metaphor,Metaheuristic
Journal
Volume
ISSN
Citations 
23
1877-7503
0
PageRank 
References 
Authors
0.34
15
2
Name
Order
Citations
PageRank
Carlos Cotta141644.36
Robert Schaefer210110.99