A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks

Takfarinas Saber; David Fagan; David Lynch; Stepan Kucera; Holger Claussen; Michael O’Neill

doi:10.1007/978-3-030-04070-3_18

A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks

Takfarinas Saber
, David Fagan
, David Lynch
, Stepan Kucera
, Holger Claussen
, Michael O’Neill

University College Dublin
Nokia

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.

Original language	English
Title of host publication	Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings
Editors	Carlos Martín-Vide, Miguel A. Vega-Rodríguez, David Fagan, Michael O’Neill
Publisher	Springer Verlag
Pages	225-237
Number of pages	13
ISBN (Print)	9783030040697
DOIs	https://doi.org/10.1007/978-3-030-04070-3_18
Publication status	Published - 2018
Externally published	Yes
Event	7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018 - Dublin, Ireland Duration: 12 Dec 2018 → 14 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11324 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018
Country/Territory	Ireland
City	Dublin
Period	12/12/18 → 14/12/18

Keywords

Genetic programming
Heterogeneous network
Hierarchical grammar-guided genetic programming
Telecommunications

Access to Document

10.1007/978-3-030-04070-3_18

Cite this

Saber, T., Fagan, D., Lynch, D., Kucera, S., Claussen, H., & O’Neill, M. (2018). A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks. In C. Martín-Vide, M. A. Vega-Rodríguez, D. Fagan, & M. O’Neill (Eds.), Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings (pp. 225-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11324 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04070-3_18

Saber, Takfarinas ; Fagan, David ; Lynch, David et al. / A Hierarchical approach to grammar-guided genetic programming : The case of scheduling in heterogeneous networks. Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings. editor / Carlos Martín-Vide ; Miguel A. Vega-Rodríguez ; David Fagan ; Michael O’Neill. Springer Verlag, 2018. pp. 225-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.",

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Saber, T, Fagan, D, Lynch, D, Kucera, S, Claussen, H & O’Neill, M 2018, A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks. in C Martín-Vide, MA Vega-Rodríguez, D Fagan & M O’Neill (eds), Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11324 LNCS, Springer Verlag, pp. 225-237, 7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018, Dublin, Ireland, 12/12/18. https://doi.org/10.1007/978-3-030-04070-3_18

A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks. / Saber, Takfarinas; Fagan, David; Lynch, David et al.
Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings. ed. / Carlos Martín-Vide; Miguel A. Vega-Rodríguez; David Fagan; Michael O’Neill. Springer Verlag, 2018. p. 225-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11324 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - A Hierarchical approach to grammar-guided genetic programming

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AU - Saber, Takfarinas

AU - Fagan, David

AU - Lynch, David

AU - Kucera, Stepan

AU - Claussen, Holger

AU - O’Neill, Michael

PY - 2018

Y1 - 2018

N2 - Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.

AB - Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.

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

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A2 - Vega-Rodríguez, Miguel A.

A2 - Fagan, David

A2 - O’Neill, Michael

PB - Springer Verlag

Y2 - 12 December 2018 through 14 December 2018

ER -

Saber T, Fagan D, Lynch D, Kucera S, Claussen H, O’Neill M. A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks. In Martín-Vide C, Vega-Rodríguez MA, Fagan D, O’Neill M, editors, Theory and Practice of Natural Computing - 7th International Conference, TPNC 2018, Proceedings. Springer Verlag. 2018. p. 225-237. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04070-3_18

A Hierarchical approach to grammar-guided genetic programming: The case of scheduling in heterogeneous networks

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