Topology of social networks and efficiency of collective intelligence methods   [SN] [CI]

by

Godoy, A. and Zuben, F., J., V.

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Info: GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion (Conference proceedings), 2013, p. 1415-1422
Abstract:
In this work we analysed the role social networks play [SN] in the efficiency of collective problem-solving, evaluating whether the topological characteristics seen in real-world networks yield any performance improvement in such processes. To study this we used the Particle Swarm Optimisation [PS] [PSO] [SO] as a testbed for social groups performing a collective task, defining the structure of communication between individuals in the swarm through topologies generated by a model for the creation and evolution of social networks. [SN] The experimental results indicate that groups using these networks may, indeed, experience better performance in collective problem-solving, so that these groups were able to overcome the results achieved by swarms using classical neighbourhoods for PSO and reached results very close to those found by swarms using the topology of DMS-PSO, usually considered to be part of the state-of-the-art of Particle Swarm Optimization. [PS] [PSO]
Notes:
Also known as cite{2482721} Distributed at GECCO-2013.
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BibTex:
@inproceedings{Godoy:2013:GECCOcomp,
 author = {Alan Godoy and Fernando J. Von Zuben},
 title = {Topology of social networks and efficiency of collective intelligence methods},
 booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion},
 year = {2013},
 editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi},
 isbn = {978-1-4503-1964-5},
 keywords = {},
 pages = {1415--1422},
 month = {6-10 July},
 organisation = {SIGEVO},
 address = {Amsterdam, The Netherlands},
url = {http://doi.acm.org/10.1145/2464576.2482721},
 doi = {doi:10.1145/2464576.2482721},
 publisher = {ACM},
publisher_address = {New York, NY, USA},
 abstract = {In this work we analysed the role social networks play in the efficiency of collective problem-solving, evaluating whether the topological characteristics seen in real-world networks yield any performance improvement in such processes. To study this we used the Particle Swarm Optimisation as a testbed for social groups performing a collective task, defining the structure of communication between individuals in the swarm through topologies generated by a model for the creation and evolution of social networks. The experimental results indicate that groups using these networks may, indeed, experience better performance in collective problem-solving, so that these groups were able to overcome the results achieved by swarms using classical neighbourhoods for PSO and reached results very close to those found by swarms using the topology of DMS-PSO, usually considered to be part of the state-of-the-art of Particle Swarm Optimization.},
 notes = {Also known as cite{2482721}
Distributed at GECCO-2013.},
 }