Ricerc@Sapienza

The herein proposed DESIGN (Distributed Evolutionary Swarm Intelligence and Granular Computing Techniques for Nested Complex Systems Modelling) project aims in defining, developing and implementing a general framework for complex systems modelling. Our basic hypothesis is that searching for regularities in data coming from the input-output sampling of the process to be modeled can be carried out by a set of agents' swarms, in charge to perform a hierarchical information granulation. Each agent performs a clustering procedure, or even more advanced information granulation tasks, on subsets of entities belonging to the previous level. An evolutionary computation orchestration coordinates the swarms in finding pivotal information granules (symbols) extracted automatically from the training set, aiming to identify suitable embedding spaces where the final classification models can be trained. The whole synthesis procedure is driven by a performance measure computed on a validation set. A backtracking mechanism, supported by consensus procedures and based on a penalty/reward strategy, is in charge to update the fitness of each information granule, as well of agents that contributed to spawning the fittest granules.
The whole machine learning algorithm is conceived to deal directly with unconventional, structured domains, such as fully labeled graphs and sequences, considered herein as the most suitable way to gather samplings coming from complex systems.
DESING aims in developing a software library for rapid application development of complex systems modelling algorithms. In order to test the effectiveness of the proposed machine learning approach, three different vertical applications will be tackled, coming from the areas of cybersecurity, bioinformatics and precision medicine, predictive maintenance on power grids.