Distributed evolutionary approach to data clustering and modeling

In this article we describe a framework (DEGA-Gen) for the application of distributed genetic algorithms for detection of communities in networks. The framework proposes efficient ways of encoding the network in the chromosomes, greatly optimizing the memory use and computations, resulting in a scalable framework. Different objective functions may be used for producing division of network into communities. The framework is implemented using open source implementation of MapReduce paradigm, Hadoop. We validate the framework by developing community detection algorithm, which uses modularity as measure of the division. Result of the algorithm is the network, partitioned into non-overlapping co…