A self-scaling instruction generator using Cartesian Genetic Programming

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

doi:10.1007/978-3-642-20407-4_26

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

3 Citations (Scopus)

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 language	English
Title of host publication	Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings
Publisher	Springer Nature
Pages	298-309
Number of pages	12
ISBN (Print)	9783642204067
DOIs	https://doi.org/10.1007/978-3-642-20407-4_26
Publication status	Published - 2011
Event	14th European Conference on Genetic Programming, EuroGP 2011 - Torino, Italy Duration: 27 Apr 2011 → 29 Apr 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6621 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th European Conference on Genetic Programming, EuroGP 2011
Country/Territory	Italy
City	Torino
Period	27 Apr 2011 → 29 Apr 2011

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10.1007/978-3-642-20407-4_26

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Liu, Y., Tempesti, G., Walker, J. A., Timmis, J., Tyrrell, A. M., & Bremner, P. (2011). A self-scaling instruction generator using Cartesian Genetic Programming. In Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings (pp. 298-309). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6621 LNCS). Springer Nature. https://doi.org/10.1007/978-3-642-20407-4_26

Liu, Yang ; Tempesti, Gianluca ; Walker, James A. et al. / A self-scaling instruction generator using Cartesian Genetic Programming. Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings. Springer Nature, 2011. pp. 298-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A self-scaling instruction generator using Cartesian Genetic Programming",

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.",

author = "Yang Liu and Gianluca Tempesti and Walker, \{James A.\} and Jon Timmis and Tyrrell, \{Andrew M.\} and Paul Bremner",

year = "2011",

doi = "10.1007/978-3-642-20407-4\_26",

language = "English",

isbn = "9783642204067",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "14th European Conference on Genetic Programming, EuroGP 2011 ; Conference date: 27-04-2011 Through 29-04-2011",

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Liu, Y, Tempesti, G, Walker, JA, Timmis, J, Tyrrell, AM & Bremner, P 2011, A self-scaling instruction generator using Cartesian Genetic Programming. in Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6621 LNCS, Springer Nature, pp. 298-309, 14th European Conference on Genetic Programming, EuroGP 2011, Torino, Italy, 27 Apr 2011. https://doi.org/10.1007/978-3-642-20407-4_26

A self-scaling instruction generator using Cartesian Genetic Programming. / Liu, Yang; Tempesti, Gianluca; Walker, James A. et al.
Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings. Springer Nature, 2011. p. 298-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6621 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

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AU - Tyrrell, Andrew M.

AU - Bremner, Paul

PY - 2011

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AB - 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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Liu Y, Tempesti G, Walker JA, Timmis J, Tyrrell AM, Bremner P. A self-scaling instruction generator using Cartesian Genetic Programming. In Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings. Springer Nature. 2011. p. 298-309. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-20407-4_26

A self-scaling instruction generator using Cartesian Genetic Programming

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