Name of Host Organization

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa

Department or Lab

MagIC (Information Management Research Center) - the NOVA IMS research and development center

Name of Building

Manuel Vilares Building

Physical Address

Campus de Campolide, 1070-312 Lisboa

Website Links

https://www.novaims.unl.pt/

Institutional contact name

Cristina Oliveira

Institutional contact email

magic@novaims.unl.pt

Principal Investigator Name

Professor Ian James Scott

Position / institutional role

Assistant Professor

ORCID

0000-0001-9699-4473

Email

iscott@novaims.unl.pt

TestBed Responsible Name
(if different from PI)

Funding source(s)
for TestBed’s acquisition

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application case:

Short description:

Combinatorial Optimisation with Genetic Algorithms

Apply genetic algorithms to hard combinatorial problems such as vehicle routing, job shop scheduling, portfolio optimisation, and resource allocation, benchmarking evolutionary approaches against exact and heuristic methods across problem instances of varying scale and complexity.

Symbolic Regression and Interpretable Model Discovery

Use genetic programming to automatically discover mathematical expressions that describe relationships in observed data, providing interpretable, equation-based models as an alternative to black-box machine learning approaches in domains where model transparency is required.

Evolutionary Algorithm Benchmarking and Reproducibility

Use the testbed to conduct systematic benchmarking of evolutionary algorithm variants across standard and domain-specific problem sets, supporting rigorous comparative studies and contributing to reproducible evolutionary computation research in line with community standards.

MSc and PhD Research in Evolutionary Computation

The platform supports graduate students conducting dissertation and research projects in evolutionary computation, metaheuristics, and automated machine learning, providing the parallel compute capacity required to run population-based searches at scales meaningful for academic publication.

Non-academic stakeholders

Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities

Academic stakeholders

MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers

Other types of stakeholders

R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

TRL application range

4

Internal academic research

Yes

Collaborative research with external academic partners

Yes

Contract research / Proof-of-Concept for industry

Yes

Pilot / DeepTech Deployment in operational environment

No

Training services (courses, workshops, certification)

Yes

Service provision (testing, benchmarking, validation)

Yes

Open access for walk-in users (e.g. open days / hackathons)

No

Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)

Yes

Type of contractual relationship

Academic partner

Industrial partner

No contract (direct access)

No

No

Direct contract between parties
(e.g., research agreement)

Yes (See Note 1)

Yes (See Note 1)

Indirect contract between parties
(e.g., project framework)

Yes (See Note 1)

Yes (See Note 1)

Type of prerequisites

Description of prerequisites

Agreements

Confidentiality agreement for proprietary algorithms

In some cases (See Note 2)  

Data sharing agreement for datasets generated

In some cases (See Note 2)

IP agreements

In some cases (See Note 2)

Other 

In some cases (See Note 3 and 4)

Insurance

Users must have appropriate liability coverage through their home institution

Yes

Mandatory technical training

N/A

Recommended technical training

Recommended training on cluster operation, job submission, queuing in SLURM.

Mandatory safety requirements

N/A

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Type

Short description:

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH01)

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH02)

Ethical and societal
aspect:

Short description:

Transparency and Interpretability as a Societal Benefit

A distinctive strength of genetic programming and symbolic regression is the production of human-readable models, in contrast to opaque deep learning approaches. This aligns with the EU AI Act's requirements for transparency and explainability in high-risk AI applications, and offers a principled route to deployable AI in regulated domains such as finance, healthcare, and public administration where interpretable decision rules are required.

Responsible Optimisation in Socially Consequential Domains

Genetic algorithms applied to problems such as workforce scheduling, resource allocation, or logistics routing can produce solutions that, while mathematically optimal, may have unequal impacts across individuals or communities. Researchers are encouraged to incorporate fairness constraints into optimisation formulations and to critically evaluate whether optimality criteria adequately reflect broader societal values.

GDPR Compliance Where Personal Data Informs Optimisation

Where evolutionary methods are applied to problems calibrated using personal or sensitive data — such as patient scheduling, employee allocation, or consumer demand modelling — researchers must ensure compliance with GDPR principles including data minimisation, purpose limitation, and lawful basis for processing, with a Data Protection Impact Assessment (DPIA) conducted where appropriate.

Supporting Industrial Competitiveness and Sustainability

Evolutionary optimisation methods have well-established applications in reducing waste, improving energy efficiency, and optimising complex industrial processes. By making these capabilities accessible to SMEs and research partners, EVOLAB supports the development of more resource-efficient operational practices aligned with European Green Deal objectives and Industry 5.0 sustainability priorities.

Reproducibility and Open Science in Evolutionary Computation

Evolutionary algorithms are stochastic by nature, making reproducibility a known methodological challenge. Researchers using EVOLAB are encouraged to report random seeds, population parameters, and termination criteria transparently, and to share code and experimental configurations in line with FAIR data principles, supporting independent verification and cumulative progress in the field.