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 de-termine 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 sub-populations regularly to further improve individual success. We use the "cone’s world" problem as test-bed. Experimental results reveal the impact of indi-vidual knowledge transfer on the target subpopula-tion’s belief space.