Autoconstructive evolution for structural problems
Kyle Harrington, Lee C. Spector, Jordan B. Pollack, Una-May O’Reilly · 2012
While most hyper-heuristics search for a heuristic that is later used to solve classes of problems, autoconstructive evolution represents an alternative which simultaneously searches both heuristic and solution space. In this study we contrast autoconstructive evolution, in which intergenerational variation is accomplished by the evolving programs themselves, with a genetic programming system, PushGP, to understand the dynamics of this hybrid approach. A problem size scaling analysis of these genetic programming techniques is performed on structural problems. These problems involve fewer domain-specific features than most model problems while maintaining core features representative of program search. We use two such problems, Order and Majority, to study autoconstructive evolution in the Push programming language.