Generating easy and hard problems using the proximate optimality principle. [Dataset]

Abstract

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.

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]

• Deposit Date: Mar 3, 2016
• 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