Advanced techniques for improving canonical genetic programming

Genetic Programming (GP) is a type of Evolutionary Algorithm (EA) commonly employed for automated program generation and model identification. Despite this, GP, as most forms of EA\u27s, is plagued by long evaluation times, and is thus generally reserved for highly complex problems. Two major impacting factors for the runtime are the heterogeneous evaluation time for the individuals and the choice of algorithmic primitives. The first paper in this thesis utilizes Asynchronous Parallel Evolutionary Algorithms (APEA) for reducing the runtime by eliminating the need to wait for an entire generation to be evaluated before continuing the search. APEA is applied to Cartesian Genetic Programming and is successful in reducing the runtime with sufficiently complex problems. The second paper in this thesis introduces Primitive Granularity Control (PGC), a method for reducing the impact and importance of the choice of primitives by allowing the primitive set to change throughout the course of evolution. Evidence is presented that demonstrates the potential for PGC to improve the quality of solutions, reduce the runtime of the algorithm, or both. However, the evidence was obtained via an exhaustive search, and how to effectively utilize PGC still requires research --Abstract, page iv