Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster

Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large number of resources simultaneously, which can significantly reduce the elapsed wall clock time per GP run. This paper explores the performance characteristics of an MPI version of the Genetic Programming Environment for FIFTH (GPE5) on a high performance computing cluster. The implementation is based on the island model with each node running the GP algorithm asynchronously. In particular, we examine the effect of several configurable properties of the system including the ratio of migration to crossover, the migration cycle of programs between nodes, and the number of processors used. The problems employed in the study were selected from the fields of symbolic regression, finite algebra, and digital signal processing. Categories and Subject Descriptors I.2.2 [Artificial Intelligence]: Automatic Programming â€...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Kenneth Holladay
• Keywords: Artificial Intelligence, Digital Signal Processing, GECCO 2009, Genetic Programming, Genetic Programming Environment