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HeuristicLab Experimental Add-ons

Besides daily builds of the trunk we also provide daily builds of some selected plugins as experimental add-ons. These add-ons contain additional features for HeuristicLab which haven't yet been integrated in the trunk and are often under heavy development. Though these features haven't yet reached maturity, they may be useful for some people.

Please note that add-ons are always developed against the trunk, so you need to either compile the trunk version of HeuristicLab by yourself or download the latest daily build from this location.

Below is the list of add-ons for which we provide daily builds as well as the links to the download location. The file, which you get when you click on the download link, is a zip file containing additional plugins. Extract these files in your HeuristicLab folder and restart HeuristicLab. The plugins will be automatically discovered and the new functionality can then be used.

Logistic Network Planning (BioBoost project)

In the EU FP-7 project BioBoost, members of HEAL have developed additional plugins for the optimization of large-scale logistic networks. These plugins are available in this package. The features include: map of EU-28 on NUTS-3 granularity. Realistic transport cost estimation using distance matrices. Optimization of two-echelon value-chains (feedstock -> preprocessing plant -> central processing plant) where the locations and capacities of plants and utilization of feedstock potential is optimized regarding either total cost or return on investment.

Meta-Optimization and Parameter Grid Tests

The meta-optimization add-on adds the meta-optimization problem to HeuristicLab. This problem allows to use heuristic optimization algorithms to find optimal parameter settings for heuristic optimization algorithms. It's also possible to let HeuristicLab automatically create experiments to test large ranges of parameters (parameter grid tests).

Fitness Landscape Analysis

The fitness landscape analysis add-on contains a set of algorithms and analyzers that can be used together or independently to explore the fitness landscape of arbitrary problems. Additionally, a memory efficient implementation of large NK landscapes and support for several vehicle routing mutation operators are included.

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Last modified 6 years ago Last modified on 02/09/18 00:44:55

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