A Self-Scaling Instruction Generator Using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Liu:2011:EuroGP,

• author = "Yang Liu and Gianluca Tempesti and James A. Walker and Jon Timmis and Andrew M. Tyrrell and Paul Bremner",
• title = "A Self-Scaling Instruction Generator Using Cartesian Genetic Programming",
• booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011",
• year = "2011",
• month = "27-29 " # apr,
• editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado",
• series = "LNCS",
• volume = "6621",
• publisher = "Springer Verlag",
• address = "Turin, Italy",
• pages = "298--309",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming: poster",
• isbn13 = "978-3-642-20406-7",
• DOI = "doi:10.1007/978-3-642-20407-4_26",
• 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.",
• notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011",

}

Genetic Programming entries for Yang Liu Gianluca Tempesti James Alfred Walker Jon Timmis Andrew M Tyrrell Paul Bremner

Citations