DOI: 10.5281/zenodo.17950641
title: The Spiking Way to Evolve - Experiment Codes and Dataset

description: This repository contains datasets and scripts generated from experiments conducted using a neuromorphic optimisation framework

The materials are provided solely to support anonymous peer review. Full details of the experimental protocols and results are described in the corresponding manuscript.

About This Repository

This Zenodo repository serves exclusively for reproducibility, providing:



Raw experimental data from all performed benchmark runs.

Experimental configurations and parameter files.

Scripts and processing routines used to generate the experimental figures and tables reported in the paper.


The comprehensive experiments assess the functionality, scalability, and runtime performance of the NeurOptimiser framework across the BBOB test suite, utilising both linear and Izhikevich spiking neuron models. Nevertheless, the information provided here can be easily adapted to other neuromorphic optimisation algorithms and problems.

What is inside this Zenodo Repository?

This repository is organised into two main components: datasets and scripts.

Datasets



exconf.zip: YAML configuration files used to launch each batch of experiments.

exdata-ioh.zip: Resulting plots from experiments conducted using exp_00-ioh.py, which implements the BBOB test suite from IOHexperimenter.

exdata-coco.zip: Raw results dataset generated using exp_01-coco.py script along with the exp_01-coco-*.yaml configuration files from exconf.zip.

ppdata-coco.zip: Postprocessed datasets generated by cocopp from COCO platform employing the raw results from exdata-coco.zip.

exdata-time.zip: Raw data for timing analysis and runtime scalability evaluation, generated by exp_01-coco.py with the time_*.yaml configuration files from exconf.zip.


Scripts



neuroptimiser.zip: A frozen version of the framework repository sufficient to reproduce the reported experiments is included.

Example0.ipynb: Jupyter notebook showing how to implement the simplest optimisation procedure using the NeurOptimiser framework.

Example1.ipynb: Jupyter notebook showing how to implement a neuroptimiser, using default parameters, to solve a BBOB problem from IOH.

Example2.ipynb: Jupyter notebook showing how to implement a neuroptimiser, using different parameters, to solve a BBOB problem from COCO.

exp_00-ioh.py: Script to run the experiments and generate the raw data and plots from experiments with IOHexperimenter. Results are saved in exdata-ioh.zip.

exp_01-coco.py: Script to run the experiments and generate raw data and plots from experiments with COCO. Raw and postprocessed results are saved in exdata-coco.zip and ppdata-coco.zip, respectively. This script requires YAML configuration files from exconf.zip to run the experiments.

 

python exp_01-coco.py ./exconf/toy.yaml 1 1 
# Args: <config_file> <num_batches> <batch>

 


exp_02-time.ipynb: Jupyter notebook for timing analysis and runtime scalability evaluation. The raw data used in this notebook is saved in exdata-time.zip, which was also generated with the exp_01-coco.py and with the time_*.yamlconfiguration files in exconf.zip.


Related Publication

The experiments associated with this dataset are described in scientific publications that cite it. Please refer to the corresponding publication for detailed methodology and results.
description: This repository does not include the complete implementation of the optimisation framework.

All external references and identifying information have been removed for anonymous peer review. A frozen version of the framework sufficient to reproduce the reported experiments is included.