This repository contains the source-code and data of the experiments from the paper A Meta-Learning Algorithm Selection Approach for the Quadratic Assignemnt Problem, presented at the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC) 2018.
The content of each folder is described next.
This folder contains the source-codes of the meta-heuristics with their default parameters. It is also provided the set of seeds used for the experiments and the runner scripts for the landscape features extraction.
These are the generated classification datasets. The full and cleansed versions are given, along with the datasets used in the final cascade scheme.
It contains the results metrics, such as the Accuracy and F-Scores, of the cascaded model given by the script cascade_classifier.py, which performs a KFold Cross Validation using the best sets of features for each cascade level.
These are all the instances retrieved from QAPLIB.