Please use this identifier to cite or link to this item: http://hdl.handle.net/11667/85
Appears in Collections:University of Stirling Research Data
Title: Data from "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study"
Creator(s): Veerapen, Nadarajen
Daolio, Fabio
Ochoa, Gabriela
Tomassini, Marco
Contact Email: nve@cs.stir.ac.uk
Keywords: Local Optima Network
Number Partitioning Problem
Fitness Landscape
Local Search
Date Available: 20-Jan-2017
Citation: Veerapen, N; Daolio, F; Ochoa, G; Tomassini, M (2017): Data from "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study". University of Stirling. Dataset. http://hdl.handle.net/11667/85
Publisher: University of Stirling
Dataset Description (Abstract): The dataset contains landscape data for "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study", G. Ochoa, N. Veerapen, F. Daolio, M. Tomassini. The 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), 19-21 April 2017, Amsterdam, The Netherlands. The dataset describes the network structure of the local optima networks (LON) for Number Partitioning Problem (NPP) instances of size 10, 15 and 20, and with values of parameter k from 0.4 to 1.2 in steps of 0.1. Dedicated UnZip software is recommended for accessing the dataset, for example, IZArc.
Dataset Description (TOC): The dataset contains landscape data for "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study", G. Ochoa, N. Veerapen, F. Daolio, M. Tomassini. The 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), 19-21 April 2017, Amsterdam, The Netherlands. The dataset describes the network structure of the local optima networks (LON) for Number Partitioning Problem (NPP) instances of size 10, 15 and 20, and with values of parameter k from 0.4 to 1.2 in steps of 0.1. The files are compressed within npp_phasetransitions_lon.zip and details on how to interpret the contents are given in readme.txt.
Type: dataset
Contract/Grant Title: The Cartography of Computational Search Spaces
DAASE: Dynamic Adaptive Automated Software Engineering
Funder(s): EPSRC - Engineering and Physical Sciences Research Council
Leverhulme Trust
Contract/Grant Number: RPG-2015-395
EP/J017515/1
URI: http://hdl.handle.net/11667/85
Rights: Rights covered by the standard CC-BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/
Affiliation(s) of Dataset Creator(s): University of Stirling
University of Lausanne

Files in This Item:
File Description SizeFormat 
readme.txt3.01 kBTextView/Open
npp_phasetransitions_lon.zip7.29 GBUnknownView/Open


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