Reference : NK Landscape Instances Mimicking the Protein Inverse Folding Problem Towards Future B...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/25028

Title :	NK Landscape Instances Mimicking the Protein Inverse Folding Problem Towards Future Benchmarks
Language :	English
Author, co-author :	Nielsen, Sune Steinbjorn [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Danoy, Grégoire [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Talbi, El-Ghazali []
Publication date :	2015
Main document title :	GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
Publisher :	ACM
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-4503-3488-4
City :	New York
Country :	USA
Event name :	The Genetic and Evolutionary Computation Conference (GECCO 2015)
Event date :	from 11-07-2015 to 12-07-2015
Event place (city) :	Madrid
Event country :	Spain
Keywords :	[en] NK Landscape ; Genetic Algorithm ; Benchmark function
Abstract :	[en] This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing on a simpler secondary structure version. Through landscape analysis tests, numerous problem properties are identified and used to parameterise and validate model instances in terms of epistatic links, adaptive- and random walk characteristics. Then the performance of different Genetic Algorithms (GAs) is compared on both the new NK Models and the original IFP, in terms of population diversity, solution quality and convergence characteristics. It is demonstrated that very similar properties are captured in all presented tests with a significantly faster evaluation time compared to the real IFP. The future purpose of such a model is to provide a generic benchmark for algorithms targeting protein sequence optimisation, specifically in protein design. It may also provide the foundation for more in-depth studies of the size, shape and characteristics of the solution space of good solutions to the IFP.
Research centres :	University of Luxembourg: High Performance Computing - ULHPC
Funders :	Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/25028

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