Computational intelligence techniques for communities network formation
Abstract
Purpose
The purpose of this paper is to analyse the role of computational intelligence techniques in the process of communities' formation.
Design/methodology/approach
The paper develops a high performance genetic algorithm for community formation based on collective intelligence capacity. An experimental study is presented to illustrate the algorithm.
Findings
Collective intelligence does not represent the sum of individual intelligences, it is the ability of the community to complete more tasks than single individuals. The paper reveals the need for mechanisms that allow a large group of professionals to make decisions better than single individuals.
Practical implications
The genetic algorithm proposed in the paper may be used to obtain the optimal structure of a community, in terms of number of members and their role in the community.
Originality/value
The key concept is a new fitness index, an intelligence index, which is the optimal combination between intelligence and cooperation, and allows not only community formation, but also intelligence to be the driving principle in the community formation process.
Keywords
Citation
Maries, I. and Scarlat, E. (2012), "Computational intelligence techniques for communities network formation", Kybernetes, Vol. 41 No. 5/6, pp. 599-610. https://doi.org/10.1108/03684921211243266
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited