The origin of life involved complicated evolutionary processes.Computer modeling is a promising way to reveal relevant mechanisms.However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling.Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values "supporting" a Pet Accessories hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available).
Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular "Methods" in a paper.Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters.With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful.In particular, based on this, it is expected that more near-reality Intimates (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field-hopefully leading to significant steps forward in respect to our understanding on the origin of life.