Knowledge Sharing Through Agent Migration with Multi-Population Cultural Algorithm

This study presents a new method for knowledge transfer in Multi-Population Cultural Algorithms (MPCA) through agent migration. This agent-based algorithm involves having individual agents using one of multiple pre-defined knowledge algorithms to determine behavior, and using the success of it and other agents to decide on which knowledge algorithms to use next. Two or more subpopulations with their own knowledge algorithm are created. The agents work in the same environment by only communicating with agents within their own subpopulation, and with two global belief spaces monitoring the effectiveness of each subpopulation. Agents transfer between the subpopulations regularly to further improve individual success. We use the as testbed. Experimental results reveal the impact of individual knowledge transfer on the target subpopula.