In this paper, we propose a method for eliciting constraints for arbitrary Domain-Specific Languages (DSL) in Program Synthesis search. We argue that we can successfully predict constraints using a form of attribute-based induction. We also provide a novel approach to constraint verification using genetic algorithms to optimize desired results. We implement our approach into GENERALIZE, a novel algorithm for reducing DSL size. GENERALIZE is tested and compared against the default Brute algorithm using 2 different program synthesis domains, robot planning and pixel art. These experiments show that GENERALIZE does not improve performance if good objective functions are available, because of a tendency to get stuck in local heuristic minima. It can increase performance if no such function is available.