Copyright © 2012 Rong-Chang Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Grouping based on social relationships is a complex problem since the social relationships within a group usually form a complicated network. To solve the problem, a novel approach which uses a combined sociometry and genetic algorithm (CSGA) is presented. A new nonlinear relation model derived from the sociometry is established to measure the social relationships, which are then used as the basis in genetic algorithm (GA) program to optimize the grouping. To evaluate the effectiveness of the proposed approach, three real datasets collected from a famous college in Taiwan were utilized. Experimental results show that CSGA optimizes the grouping effectively and efficiently and students are very satisfied with the grouping results, feel the proposed approach interesting, and show a high repeat intention of using it. In addition, a paired sample t-test shows that the overall satisfaction on the proposed CSGA approach is significantly higher than the random method.