Combining analytical and evolutionary inductive programming

Abstract

Analytical inductive programming and evolutionary inductive programming are two opposing strategies for learning recursive programs from incomplete specifications such as input/output examples. Analytical inductive programming is data-driven, namely, the minimal recursive generalization over the positive input/output examples is generated by recurrence detection. Evolutionary inductive programming, on the other hand, is based on searching through hypothesis space for a (recursive) program which performs sufficiently well on the given input/output examples with respect to some measure of fitness. While analytical approaches are fast and guarantee some characteristics of the induced program by construction (such as minimality and termination) the class of inducable programs is restricted to problems which can be specified by few positive examples. The scope of programs which can be generated by evolutionary approaches is, in principle, unrestricted, but generation times are typically high and there is no guarantee that such a program is found for which the fitness is optimal. We present a first study exploring possible benefits from combining analytical and evolutionary inductive programming. We use the analytical system IGOR2 to generate skeleton programs which are used as initial hypotheses for the evolutionary system ADATE;. We can show that providing such constraints can reduce the induction time of ADATE.