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

^*Awdur cyfatebol y gwaith hwn

Adran Cyfrifiadureg

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

3 Dyfyniadau (Scopus)

Crynodeb

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.

Iaith wreiddiol	Saesneg
Teitl	Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings
Cyhoeddwr	Springer Nature
Tudalennau	298-309
Nifer y tudalennau	12
ISBN (Argraffiad)	9783642204067
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-642-20407-4_26
Statws	Cyhoeddwyd - 2011
Digwyddiad	14th European Conference on Genetic Programming, EuroGP 2011 - Torino, Yr Eidal Hyd: 27 Ebr 2011 → 29 Ebr 2011

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol	6621 LNCS
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

Cynhadledd

Cynhadledd	14th European Conference on Genetic Programming, EuroGP 2011
Gwlad/Tiriogaeth	Yr Eidal
Dinas	Torino
Cyfnod	27 Ebr 2011 → 29 Ebr 2011

Mynediad at Ddogfen

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

Cysylltiad parhaol

Dolen barhaus

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

Liu, Y., Tempesti, G., Walker, J. A., Timmis, J., Tyrrell, A. M., & Bremner, P. (2011). A self-scaling instruction generator using Cartesian Genetic Programming. Yn Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings (tt. 298-309). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 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. tt. 298-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{58176bd9af94427e93c43b2341a92365,

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)",

publisher = "Springer Nature",

pages = "298--309",

booktitle = "Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings",

address = "Switzerland",

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

}

Liu, Y, Tempesti, G, Walker, JA, Timmis, J, Tyrrell, AM & Bremner, P 2011, A self-scaling instruction generator using Cartesian Genetic Programming. yn 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), cyfrol. 6621 LNCS, Springer Nature, tt. 298-309, 14th European Conference on Genetic Programming, EuroGP 2011, Torino, Yr Eidal, 27 Ebr 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. t. 298-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 6621 LNCS).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

T1 - A self-scaling instruction generator using Cartesian Genetic Programming

AU - Liu, Yang

AU - Tempesti, Gianluca

AU - Walker, James A.

AU - Timmis, Jon

AU - Tyrrell, Andrew M.

AU - Bremner, Paul

PY - 2011

Y1 - 2011

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=79955766422&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-20407-4_26

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

M3 - Conference Proceeding (Non-Journal item)

AN - SCOPUS:79955766422

SN - 9783642204067

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 309

BT - Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings

PB - Springer Nature

T2 - 14th European Conference on Genetic Programming, EuroGP 2011

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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. Yn Genetic Programming - 14th European Conference, EuroGP 2011, Proceedings. Springer Nature. 2011. t. 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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