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.

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