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Parameter Variation Experiments in Upcoming HeuristicLab Release

We've included a new feature in the upcoming release of HeuristicLab 3.3.7 that will make it more comfortable to create parameter variation experiments.

Metaheuristics and data analysis methods often have a number of parameters which highly influence their behavior and thus the quality that you obtain in applying them on a certain problem. The best parameters are usually not known a priori and you can either use metaoptimization (available from the download page under "Additional packages") or create a set of experiments where each parameter is varied accordingly. In the upcoming release we've made this variation task a lot easier.

We have enhanced the "Create Experiments" dialog that is available through the Edit menu. To try out the new feature you can obtain the latest daily build from the Download page and load one of the samples. The dialog allows you to specify the values of several parameters and allows you to create an experiment where all configurations are enumerated.

We have also included the new problem instance infrastructure in this dialog which further allows you to test certain configurations on a number of benchmark instances from several benchmark libraries.

Finally, here are a couple of points that you should be aware of to make effective use of this feature. You can view this as a kind of checklist, before creating and executing experiments:

  • Before creating an experiment make sure you prepare the algorithm accordingly, set all parameter that you do not want to vary to the value that you intend. If the algorithm contains any runs, clear them first.
  • Review the selected analyzers carefully, maybe you want to exclude the quality chart and some other analyzers that would produce too much data for a large experiment. Or maybe you want to output the analyzers only every xth iteration.
  • Make sure you check to include in the run only those problem and algorithm parameters that you need. Think twice before showing a parameter in the run that requires a lot of memory.
  • Make sure SetSeedRandomly (if available) is set to true if you intend to repeat each configuration.
  • When you make experiments with dependent parameters you have to resolve the dependencies and create separate experiments. For example, when you have one parameter that specifies a lower bound and another that specifies an upper bound you should create separate experiments for each lower bound so that you don't obtain configurations where the upper bound is lower than the lower bound.
  • Finally, while you vary the parameters keep an eye on the number of variations. HeuristicLab doesn't prevent you from creating very large experiments, but if there are many variations you might want to create separate experiments.

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