Discovery of Subroutines in Genetic Programming
Justinian P. Rosca, Dana H. Ballard · The MIT Press eBooks · 1996
Introduction Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of programs [Koza 1992, Rosca and Ballard 1994a] . The use of subroutines biases the search for good programs and offers the possibility to reuse code. While HGP approaches improve the efficiency and scalability of genetic programming (GP) for many applications [Koza, 1994b], several issues remain unresolved. The scalability of HGP techniques could be further improved by solving two such issues. One is the characterization of the value of subroutines. Current methods for HGP do not attempt to decide what is relevant, i.e. which blocks of code or subroutines may be worth giving special attention, but employ genetic operations on subroutines at random points. The other issue is the time-course of the generation of new subroutines. Current HGP techniques do not make informed choices to automatically decide whe