PhD candidate in Artificial Intelligence at the Department of Computer Science of the Norwegian University of Science and Technology. My research interests include analysis of evolutionary computation for stochastic optimisation as well as constraint satisfaction and computability. I enjoy puzzles, maths, nature and music. Lately I have been working on a playground for evolutionary computation in Julia.

I was previously a research associate at the Research Group with Strategic Focus in Intelligent Systems of Tecnológico de Monterrey, under the bio-inspired systems line of research, and an associate lecturer in the Department of Computer Science.

I also spent some time as a research assistant and lead developer at the Department of Mathematics at Tecnológico de Monterrey, where I implemented online evaluations for two math MOOCs *Matemáticas y Movimiento* and *El Cálculo*, among many other projects focusing on mathematics education.

Interests

- Evolutionary Computation
- Constraint Satisfaction
- Artificial Intelligence
- Hyper-heuristics

Education

PhD in Artificial Intelligence, Current

Norwegian University of Science and Technology

MSc in Intelligent Systems, 2017

Tecnológico de Monterrey

BSc in Software Engineering, 2015

Universidad Autónoma de Nuevo León

.js-id-evolutionary-computation

EvoLP is a playground for evolutionary computation in Julia. It provides a set of predefined building blocks that can be coupled together to quickly generate evolutionary computation solvers and compute statistics for a variety of optimisation tasks, including discrete, continuous and combinatorial optimisation.

Trustworthy research is achieved through open, explainable and reproducible experiments. For this, testing and documentation are crucial. Learn about documenting your Python code.

Hyper-heuristics Hyper-heuristics can be seen as “algorithms to select or generate new algorithms”.
They can be used to find feasible or approximate solutions for NP-hard optimisation problems. Please feel free to look at the further reading section in the slides and hit me up if you have any questions.

Please see the slides for a more detailed explanation.
DEAP Short Tutorial DEAP (Distributed Evolutionary Algorithms for Python) is a Python module which provides you with tools to easily implement evolutionary computation algorithms.

A simple webpage to typeset equations in LaTeX using MathJax. Useful to practise and learn common LaTeX commands and symbols, as you can see both results and the commands side by side.

A very brief and visual introduction to the Git methodology, from a more academic point of view. Highly recommended to understand how Git works and how it could help in both your personal or collaborative projects.

For a comprehensive list, please refer to my Google Scholar.

Evolutionary Computation with Islands: Extending EvoLP.jl for Parallel Computing.
NIKT: Norsk IKT-konferanse for forskning og utdanning.

(2023).
Controlling Hybrid Evolutionary Algorithms in Subset Selection for Multimodal Optimization.
GECCO ‘23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. July 2023. Pages 507–510.

(2023).
Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation Study.
GECCO ‘23: Proceedings of the Genetic and Evolutionary Computation ConferenceJuly 2023. pp. 1454–1463.

(2023).
EvoLP.jl: A playground for Evolutionary Computation in Julia.
Proceedings of the 5th Symposium of the Norwegian AI Society, 3431. CEUR Workshop Proceedings Vol. 3431.

(2023).
A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem.
A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem.

(2021).
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