Optimizing Agents With Genetic Programming - An Evaluation Of Hyper-Heuristics In Dynamic Real-Time Logistics - Datasets And Results

This directory contains the data and results that we used and obtained during the realization of the following paper: Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017). The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130. This repository contains the following files: evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment. JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760 realtime.zip (expands to 913.5 MB) A zip file containing the main results dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments. overview.zip (expands to 42 MB) Contains the following files: overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper. overview/heuristics/ - folder with a tree visualization for every evolved heuristic. overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/. readme.txt - this file. overview/results/ - contains the scripts overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.