Improving artifact selection via agent migration in multi-population cultural algorithms

Multi-population cultural algorithms are cultural evolutionary frameworks involving multiple independently evolving subpopulations. Artifact selection involves the ability of agents to autonomously reason about selecting artifacts towards achieving their goals. In this study, agent migration between populations in a multi-population cultural algorithm is explored as an approach for augmenting artifact selection knowledge in social agents. Embedded in a social simulation model the multipopulation cultural algorithm consists of two subpopulations where agents in one subpopulation consistently outperform agents in the other due to the presence of knowledge about certain artifacts. Social networks connect agents within a subpopulation and agent knowledge can be altered by members of their network or the best performers of their subpopulation. The model investigates agent migration with novel artifact knowledge from the advanced subpopulation to the underperforming one. Child safety restraint selection is provided as an implemented case study. Results demonstrate the benefits of migration with a higher likelihood of an increase in agent performance when the social network is enabled. The study shows that culturally evolving agents can improve artifact selection knowledge in the absence of standard interventions as a result of migration.