Professor John McCall j.mccall@rgu.ac.uk
Data Collector
Professor John McCall j.mccall@rgu.ac.uk
Data Collector
Dr Lee Christie l.a.christie@rgu.ac.uk
Data Collector
Alexander Edward Ian Brownlee
Data Collector
These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of length 6-100 and the same number of anti-coherent functions using the same seed sets. Seed sets were generated by uniformly at random sampling 50 distinct points from the search space. For each function we ran a multi-restart steepest ascent hillclimber 100 times and recorded the time taken to solve the problem as a function of problem size. The data gathered is used to plot the average number of evaluations required by the hillclimber to solve each function against bit-string length. This process confirmed our hypothesis. The data are visualised in figure 2 of the related publication, linked below.
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]
Deposit Date | Mar 3, 2016 |
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Publicly Available Date | Mar 3, 2016 |
Keywords | Problem generation; Proximate; Optimality; Estimation of distribution algorithms |
Public URL | http://hdl.handle.net/10059/1407 |
Related Public URLs | http://hdl.handle.net/10059/1384 ; http://hdl.handle.net/10059/1406 ; http://hdl.handle.net/10059/1567 ; http://hdl.handle.net/10059/1585 |
Type of Data | CSV file. |
Collection Date | Dec 31, 2015 |
Contract Date | Mar 3, 2016 |
MCCALL 2015 Generating easy and hard problems (DATASET)
(906 Kb)
Archive
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
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Presentation / Conference Contribution
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