Created by W.Langdon from gp-bibliography.bib Revision:1.5628
This thesis investigates genetic programming as a method to automatically evolve various classes of automata including finite acceptors, pushdown automata, finite state transducers and Turing machines for benchmark sets of problems. A new approach to evolving automata is introduced whereby each individual in the GP population is represented directly as a graph as opposed to a tree. The methods for evaluation, standard and advanced GP characteristics, and the GP parameters are identified.
Genetic programming proves to be an effective method for inducing automata. The GP systems presented in this thesis successfully induce solutions for finite acceptors; pushdown automata, finite state transducer and Turing machine languages. Furthermore, it is shown that using non-destructive operators and multiple iterations improve the success rate of the GP system.",
Genetic Programming entries for Amashini Naidoo