Generating easy and hard problems using the proximate optimality principle.
McCall, John; Christie, Lee A.; Brownlee, Alexander Edward Ian
Doctor Lee Christie email@example.com
Alexander Edward Ian Brownlee
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.
|Institution Citation||MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]|
|Keywords||Problem generation; Proximate; Optimality; Estimation of distribution algorithms|
|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|
MCCALL 2015 Generating easy and hard problems (DATASET)
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