By the same authors

Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics

Research output: Contribution to conference › Other

Standard

Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics. / Swan, Jeremiah; Burles, Nathan John.

2015. 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption, London, United Kingdom.

Research output: Contribution to conference › Other

Harvard

Swan, J & Burles, NJ 2015, 'Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics', 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption, London, United Kingdom, 23/02/15 - 24/02/15.

APA

Swan, J., & Burles, N. J. (2015). Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics. 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption, London, United Kingdom.

Vancouver

Swan J, Burles NJ. Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics. 2015. 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption, London, United Kingdom.

Author

Swan, Jeremiah ; Burles, Nathan John. / Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics. 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption, London, United Kingdom.

Bibtex - Download

@conference{5a18f200cce043c58e2ba1556d84f3bf,
title = "Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics",
abstract = "Scalability remains an issue for program synthesis:- We don{\textquoteright}t yet know how to generate sizeable algorithms from scratch.- Generative approaches such as GP still work best at the scale of expressions (though some recent promising results).- Formal approaches require a strong mathematical background.- ... but human ingenuity already provides a vast repertoire of specialized algorithms, usually with known asymptotic behaviour.Given these limitations, how can we best use generative hyper-heuristics to improve upon human-designed algorithms?",
author = "Jeremiah Swan and Burles, {Nathan John}",
year = "2015",
language = "English",
note = "39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption ; Conference date: 23-02-2015 Through 24-02-2015",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics

AU - Swan, Jeremiah

AU - Burles, Nathan John

PY - 2015

Y1 - 2015

N2 - Scalability remains an issue for program synthesis:- We don’t yet know how to generate sizeable algorithms from scratch.- Generative approaches such as GP still work best at the scale of expressions (though some recent promising results).- Formal approaches require a strong mathematical background.- ... but human ingenuity already provides a vast repertoire of specialized algorithms, usually with known asymptotic behaviour.Given these limitations, how can we best use generative hyper-heuristics to improve upon human-designed algorithms?

AB - Scalability remains an issue for program synthesis:- We don’t yet know how to generate sizeable algorithms from scratch.- Generative approaches such as GP still work best at the scale of expressions (though some recent promising results).- Formal approaches require a strong mathematical background.- ... but human ingenuity already provides a vast repertoire of specialized algorithms, usually with known asymptotic behaviour.Given these limitations, how can we best use generative hyper-heuristics to improve upon human-designed algorithms?

M3 - Other

T2 - 39th CREST Open Workshop: Measuring, Testing and Optimising Computational Energy Consumption

Y2 - 23 February 2015 through 24 February 2015

ER -

York staff: edit these data