Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding

We consider simultaneous evolutionary synthesis of multiple functions, and verify whether such approach leads to computational savings compared to conventional synthesis of functions one-by-one. We also extend the proposed synthesis model with scaffolding, a technique originally intended to facilitate evolution of recursive programs, and consisting in fetching the desired output from a test case, rather than calling a function. Experiment concerning synthesis of list manipulation programs in Scala allows us to conclude that parallel synthesis indeed pays off, and that engagement of scaffolding leads to further improvements.