The GTI-IA research group has numerous open research lines. Among them, I am more interested in Multi Agent based systems and in applying Soft Computing techniques to solve various problems.
My doctoral thesis is about the application of Soft Computing techniques to solve diverse combinatorial problems. Specifically,
my thesis proposes a domain-independent search architecture, which is able to tackle large combinatorial problems, even in situations
where there is little starting data. This architecture is based on Soft Computing techniques, combining a genetic algorithm based on real
coding with artificial neural network-based models (multilayer perceptrons). Thus, the genetic algorithm makes use of these perceptrons for
fitness evaluations, when necessary. The obtained system offers the required flexibility and versatility to be able to tackle with whatever
combinatorial problem.
Furthermore, the developed Soft Computing architecture was applied to solve combinatorial problems of interest, both in the area of Combinatorial
Catalysis and in the domain of Recommender Systems. Firstly, requirements and needs of the problems to be solved within the scope of both domains
were considered. Secondly, the proposed technique was used in the field of catalysis in order to optimize the conditions for different reactions,
as well as to determine the best catalyst compositions suitable for reactions of different nature and complexity. Also, the proposed search architecture
was applied to an entertainment domain in the field of Recommender Systems: the evaluation of films. For this study,
the MovieLens dataset was employed, which is a well-known benchmark in this field. Thus, the architecture was used
to determine the preferences of certain users from the information available about them or from the information available about
others users with similar preferences.
