Genetically Evolving Models in Science (GEMS)

Project leader: Fernand Gobet

Postdoctoral Research Officers: Laura Bartlett, Noman JavedAngelo Pirrone, Hugo Stevensen.

The development of scientific models suffers from two related problems: the ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, philosophy, computer modelling, computer science and cognitive neuroscience) addresses these problems by developing a novel methodology for generating scientific models automatically. The methodology is not specific to any particular discipline and can be applied to any science where experimental data are available. The method treats models as computer programs and evolves a population of models using genetic programming. The extent to which the models fit the empirical data is used as a fitness function. The best models – potentially modified by cross-over and mutation – are selected for the next generation. Pilot simulations have established the validity of the methodology with simple experiments in psychology.


This project is funded by a five-year European Research Council (ERC) Advanced Grant.  


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