Automatic code generation on a MOVE processor using Cartesian genetic programming

Authors:
James Alfred Walker;Yang Liu;Gianluca Tempesti;Andy M. Tyrrell
Affiliations:
Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, UK;Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, UK;Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, UK;Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, UK
Venue:
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Year:
2010

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Abstract

This paper presents for the first time the application of Cartesian Genetic Programming to the evolution of machine code for a simple implementation of a MOVE processor. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with three different levels of parallelism. The results show that 100% successful solutions were found by CGP and by further optimising the size of the solutions, it is possible to find efficient implementations of the 4-bit multiplier that have the potential to be "human competitive". Further analysis of the results revealed that the structure of some solutions followed a known general design methodology.