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.
Funding of IAPMEI to support the creation of a web platform for young people with educational content.
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.
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.
Jessica Mégane and Ricardo Paiva
Project attempts to make a model of firefly flash synchronization.
Jessica Mégane and Filip Hustic
RDF file with connection between words, such as synonyms, hyperonyms and other attributes for the Portuguese language.
Jessica Cunha and João Almeida