A self-scaling instruction generator using Cartesian Genetic Programming

Yang Liu*, Gianluca Tempesti, James A. Walker, Jon Timmis, Andrew M. Tyrrell, Paul Bremner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

3 Citations (SciVal)

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.

Original languageEnglish
Title of host publicationGenetic Programming - 14th European Conference, EuroGP 2011, Proceedings
PublisherSpringer Nature
Pages298-309
Number of pages12
ISBN (Print)9783642204067
DOIs
Publication statusPublished - 2011
Event14th European Conference on Genetic Programming, EuroGP 2011 - Torino, Italy
Duration: 27 Apr 201129 Apr 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6621 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Genetic Programming, EuroGP 2011
Country/TerritoryItaly
CityTorino
Period27 Apr 201129 Apr 2011

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