About me

I'm currently doing a Master in Informatics Engineering at the University of Coimbra, with a specialization in Intelligent Systems. My master's thesis is focused on developing a new mapping mechanism for Grammatical Evolution, a branch of Genetic Programming, which is based on biological evolution to solve problems.

I also have experience in other machine learning subjects, such as pattern recognition, artificial neural networks, and deep learning, and I am deeply motivated and always looking to learn more.

In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule.

Probabilistic Grammatical Evolution

Jessica Mégane, Nuno Lourenço and Penousal Machado

In this paper we show the comparison between two algorithms in a dynamic environment. In the first one, two different populations are evolved, and from time to time, some individuals change populations. In the second algorithm, the worst individuals are replaced by new random individuals. To evaluate the performance both algorithms are tested in a dynamic environment using the Knapsack algorithm, where each 10 generations, the problem conditions are changed.

Comparison between Multi-Population Migration and Random Immigrants Algorithms

Jessica Mégane and Ricardo Paiva

Project attempts to make a model of firefly flash synchronization.

Firefly synchronization

Jessica Mégane and Filip Hustic

RDF file with connection between words, such as synonyms, hyperonyms and other attributes for the Portuguese language.

RDF with Onto.PT and SentiLex-PT data

Jessica Cunha and João Almeida