High-Performance, Parallel, Stack-Based Genetic Programming

Abstract

HiGP is a new high-performance genetic programming system. This system combines techniques from string-based genetic algorithms, S-expression-based genetic programming systems, and high-performance parallel computing. The result is a fast, flexible, and easily portable genetic programming engine with a clear and efficient parallel implementation. HiGP manipulates and produces linear programs for a stack-based virtual machine, rather than the tree-structured S-expressions used in traditional genetic programming. In this paper we describe the HiGP virtual machine and genetic programming algorithms. We demonstrate the system's performance on a symbolic regression problem and show that HiGP can solve this problem with substantially less computational effort than can a traditional genetic programming system. We also show that HiGP's time performance is significantly better than that of a well-written S-expression-based system, also written in C. We further show that our parallel version of HiGP achieves a speedup that is nearly linear in the number of processors, without mandating the use of localized breeding strategies.