Please use this identifier to cite or link to this item: http://hdl.handle.net/11667/75
Appears in Collections:University of Stirling Research Data
Title: Data from ''Tunnelling Crossover Networks for the Asymmetric TSP"
Creator(s): Veerapen, Nadarajen
Ochoa, Gabriela
Tinos, Renato
Whitley, L Darrell
Contact Email: nve@cs.stir.ac.uk
Keywords: Local Optima Network
Asymmetric Traveling Salesman Problem
Fitness Landscape
Local Search
Genetic Algorithm
Date Available: 13-Jun-2016
Citation: Veerapen, N; Ochoa, G; Tinos, R; Whitley LD (2016): Data from ''Tunnelling Crossover Networks for the Asymmetric TSP". University of Stirling. Dataset. http://hdl.handle.net/11667/75
Dataset Description (Abstract): The dataset contains landscape data for "Tunnelling Crossover Networks for the Asymmetric TSP", N. Veerapen, G. Ochoa, R. Tinós, D. Whitley, The 14th International Conference on Parallel Problem Solving from Nature, PPSN2016, 17-21 September 2016, Edinburgh, Scotland. The dataset describes the network structure of the local optima networks for the 25 Asymmetric Traveling Salesman Problem instances that are sampled in the paper according to two different methodologies: using an evolutionary algorithm based on the Generalized Partition Crossover, and using Chained Lin-Kernighan.
Dataset Description (TOC): The dataset contains landscape data for "Tunnelling Crossover Networks for the Asymmetric TSP", N. Veerapen, G. Ochoa, R. Tinós, D. Whitley, The 14th International Conference on Parallel Problem Solving from Nature, PPSN2016, 17-21 September 2016, Edinburgh, Scotland. The dataset describes the network structure of the local optima networks for the 25 Asymmetric Traveling Salesman Problem instances that are sampled in the paper according to two different methodologies: using an evolutionary algorithm based on the Generalized Partition Crossover, and using Chained Lin-Kernighan. The data are organised into three zip files, one for each method and one for generated instances. These instances (C50.0, C100.0, C200.0, E50.0, E100.0, and E200.0) were generated using the DIMACS TSP instance generator (http://dimacs.rutgers.edu/Challenges/TSP/download.html) and the distance matrices were perturbed to obtained asymmetric instances. The rest of the instances are from TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/). Additional details are provided in the readme.txt file.
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/11667/75
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 Sao Paulo
Colorado State University

Files in This Item:
File Description SizeFormat 
gapx.zipNetworks generated with the GAPX-based GA1.44 GBUnknownView/Open
gen_instances.zipGenerated Instances681.85 kBUnknownView/Open
clk.zipNetworks generated with Chained Lin-Kernighan366.57 MBUnknownView/Open
readme.txtDescription of file organisation, names and structure5.93 kBTextView/Open


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