Evolution of Graph-Like Programs with Parallel Distributed Genetic Programming.
Riccardo Poli · 1997
Parallel Distributed Genetic Programming (PDGP) is a new form of Genetic Programming (GP) suitable for the development of programs with a high degree of parallelism. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. The paper presents the representations, the operators and the interpreters used in PDGP, and describes experiments in which PDGP has been compared to standard GP. 1 Introduction In Genetic Programming (GP) [Koza, 1992, Koza, 1994] programs are expressed as parse trees to be executed sequentially. This form of GP has been applied successfully to a number of difficult problems like automated design, pattern recognition, robot control, image analysis, etc. [Koza, 1992, Koza, 1994, K. E. Kinnear, Jr., 1994]. When appropriate terminals, functions and interpreters are defined, standard GP can go beyond the production of sequential programs. For example using cellular enc...