Solving the Processor Configuration Problems with a Mutation-Based Genetic Algorithm

This paper presents a Genetic Algorithm (GA) approach to the Processor Configuration Problem that uses a mutation-based GA, a function that produces schemata by analyzing previous solutions, and an efficient data representation.

Abstract

The Processor Configuration Problem (PCP) is a real life Constraint Optimization Problem. The task is to link up a finite set of processors into a network, whilst minimizing the maximum distance between these processors. Since each processor has a limited number of communication channels, a carefully planned layout will help reduce the overhead for message switching. In this paper, we present a Genetic Algorithm (GA) approach to the PCP. Our technique uses a mutation-based GA, a function that produces schemata by analyzing previous solutions, and an efficient data representation. Our approach has been shown to out-perform other published techniques in this problem.