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A Self-scaling Instruction Generator Using Cartesian Genetic Programming

  • Yang Liu
  • Gianluca Tempesti
  • James A. Walker
  • Jon Timmis
  • Andrew M. Tyrrell
  • Paul Bremner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

In the past decades, a number of genetic programming techniques have been developed to evolve machine instructions. However, these approaches typically suffer from a lack of scalability that seriously impairs their applicability to real-world scenarios. In this paper, a novel self-scaling instruction generation method is introduced, which tries to overcome the scalability issue by using Cartesian Genetic Programming. In the proposed method, a dual-layer network architecture is created: one layer is used to evolve a series of instructions while the other is dedicated to the generation of loop control parameters.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yang Liu
    • 1
  • Gianluca Tempesti
    • 1
  • James A. Walker
    • 1
  • Jon Timmis
    • 1
    • 2
  • Andrew M. Tyrrell
    • 1
  • Paul Bremner
    • 3
  1. 1.Department of ElectronicsUniversity of YorkUK
  2. 2.Department of Computer ScienceUniversity of YorkUK
  3. 3.Bristol Robotics LaboratoryUniversity of the West of EnglandUK

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