Montag, 15.6. 16 Uhr, Raum F 303

Neil Crossley (Präsentation der Masterarbeit): Analytical Preprocessing for Evolutionary Inductive Programming 

Evolutionary programming is the most powerful method for inducing recursive functional programs from input/output examples while taking into account efficiency and complexity constraints for the target program. However, synthesis time can be considerably high. A strategy which is complementary to the generate-and -test based approaches of evolutionary programming is inductive analytical programming where program construction is example-driven, that is, target programs are constructed as minimal generalization over the given input/output examples. Synthesis with analytical approaches is fast, but the scope of synthesizable programs is restricted. We propose to combine both approaches in such a way that the power of evolutionary programming is preserved and synthesis becomes more efficient. We use the analytical system {\sc Igor2} to generate seeds in form of program skeletons to guide the evolutionary system {\sc Adate} when searching for target programs. In an evaluations with several examples we can show that using such seeds indeed can speed up evolutionary programming considerably