Automated replication optimization for protocellular information system

Due to the high cost of experiments, high dimensional complex systems with multiple parameters usually pose grand challenges not only in artificial life but in areas including manufacturing processes, supply chains as well as services in the healthcare sector. We present and verify a fully automated method of reducing the needed experiments to identify optimal operational conditions for complex systems, here tested in simulation on a protocellular information system. The method iteratively becomes better at locating system optima through an adaptive data analysis, which is an advantage over e.g. a Monte Carlo optimization method (2).