This repository contains the resulting datasets of using the hyper-heuristic based on neural networks to produce enhanced metahueristics. We also included the main jupyter file to plot the figures thereby presented.

Authors: José Manuel Tapia-Avitia, Jorge M. Cruz-Duarte, Ivan Amaya, José Carlos Ortiz-Bayliss, Hugo Terashima Marín, Nelishia Pillay

Due to the file size limitation of GitHub, we provided the resulting data files in split zip files into data_files: all-exp-results.zip, all-exp-results.z01, all-exp-results.z02, ..., all-exp-results.z33. So, after cloning this repository, you must follow the steps below in your terminal.

1. Go to the tl-hh-umhs/data_files folder:

cd data_files

2. Combine the split zip files:

zip -F all-exp-results.zip --out all-exp-results-single.zip`

3. Unzip the combined zip file:

unzip all-exp-results-single.zip

Then, you have the data result files.

• Python v3.8+
• CUSTOMHyS framework
• Standard modules: os, matplotlib, seaborn, numpy, pandas, scipy.stats, tensorflow

• Main notebook: processing_results_nnhh.ipynb
• Results from the proposed approaches (this folder will be available once unzip all-exp-results files): data_files/all-exp-results
• Raw figures generated from the main notebook: data_files/exp_figures
• Experimental configurations used in this work: exconf
• Results for basic metaheuristics: data_files/basic-metaheuristics-data_v2.json
• Collection of basic metaheuristics: collections/basicmetaheuristics.txt
• Collection of default heuristics: collections/default.txt

José Manuel Tapia-Avitia - [email protected] Jorge M. Cruz-Duarte - jcrvz.co, [email protected]

Distributed under the MIT license. See LICENSE for more information.