A study on population adaptation in social networks based on knowledge migration in cultural algorithm

摘要

Networks can be analyzed from different aspects-micro and macro. If we assume that the main asset of each network is its population and the key difference between populations is their knowledge, then it is knowledge that drives the evolution of any network. In this paper, the behavior and status of a network will be analyzed in a case where a population from one network migrates to another similar network and transfers its knowledge to it. In fact, we are going to find how a migrated population will adapt itself to a new environment with similar characteristics based on the knowledge that it has learned from the previous network and what is the role of this prior knowledge in its evolution. For this purpose, different scenarios are modeled by employing a cultural algorithm with various networks and populations on two different cases: a population with migrated knowledge and a population without it. The results clearly show that when the changes in the structure of networks are less than 25%, trained population can adapt itself with the new network very fast but when the difference is higher, in the best case they perform like a random population without any training.