Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains.