Mini-Project 2 : Java-based Distributed Genetic Programming



Supervised by Dr W. B. Langdon.

Abstract of my mini-project report

The increased availability of the Internet presents a wealth of computing resources which, when harnessed in the implementation of genetic programming, provides the computing power required for solving difficult problems. This paper proposes a distributed approach to genetic programming. A distributed genetic programming system termed DGP is developed in the Java programming language to demonstrate the potential of distributed genetic programming running over a network of heterogeneous computers. This paper describes the design and implementation of the DGP system. Experiments were conducted to compare the performances of distributed genetic programming and conventional genetic programming on three problems. The results of the experiments are reported in this paper.


The DGP system



Description

The DGP system system was adapted from a standalone GP system written by Hansueli Gerber to solve the simple symbolic regression problem. The implementation of DGP consists of several sequential genetic programs running aschronously on a network of workstations, trying to maximise the same function. On each workstation, a local population is resident, which evolves independently of the populations on the other workstations. After a designated number of generations, each workstation will transmit their best solutions to neighbouring subpopulations. On receiving immigrants from other populations, the workstation will select and replace its worst individual with these immigrants. This migration of individuals refreshes the genetic material of neighbouring populations. Instructions for running the DGP system is here.

Features






This page has had hits since 13 July 1998.

Last updated on 28/06/98