Bag of Baselines
Bag of Baselines implements several multi-objective opimisation methods to create a performance benchmark on two small datasets. To learn more about this work, check out the publication.
Methods
The following methods are proposed and implemented:
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SH-EMOA: Speeding up Evolutionary Multi-Objective Algorithms
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MO-BOHB: Generalization of BOHB to an Arbitrary Number of Objectives
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MS-EHVI: Mixed Surrogate Expected Hypervolume Improvement
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MO-BANANAS
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BULK & CUT
Datasets
Performance of the methods was evaluated using the following datasets: Oxford-Flowers dataset and Fashion-MNIST.
Organization
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The specific code for each of the methods (the main logic of each algorithm) is stored in the methods folder.
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In the examples folder you will find a small Python script to run each of the available methods (for the "Fashion-MNIST" or the "flowers" dataset).
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Code defining the search space and the evaluation function of the two different problems are defined in the problems folder.