Automatic discovery of scientific laws in observed data by asynchronous parallel evolutionary algorithm

How to discover high-level knowledge such as laws of natural science in observed data automatically is a very important and difficult task in scientific research. High level knowledge modeled by ordinary differential equations (ODES) is discovered in observed dynamic data automatically by an asynchronous parallel evolutionary algorithm called AP-HEMA. A numerical example is used to demonstrate the potential of AP-HEMA. The results show that the dynamic models discovered automatically in the observed dynamic data by computer can sometimes compare with models discovered by humans.