Genetic parallel programming: design and implementation

  • Authors:

  • Sin Man Cheang;Kwong Sak Leung;Kin Hong Lee

  • Affiliations:

  • Department of Computing, Hong Kong Institute of Vocational Education, Kwai Chung, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong

  • Venue:
  • Evolutionary Computation
  • Year:
  • 2006

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Abstract

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.