Giovanni Giachetti

Towards Autonomous Bio-Inspired Optimization: Deep Reinforcement Learning for Adaptive Metaheuristic Orchestration

Learning-based hyper-heuristics for combinatorial optimization select from among low-level operators or tune the parameters of a single metaheuristic. However, online selection among complete population-based metaheuristics that share one evolving population and its joint configuration through a common action space hav...

MATIIGEN: A Model-Driven Framework for Automated DNA Variant Interpretation in Precision Medicine

Integrating Architectural Metamodels for Model-Based Testing of Large Language Models

This paper presents a preliminary integrated approach that operationalizes an architecture metamodel across the four stages of an MBT framework pipeline: Modeling, Test Case Generation, Test Script Generation, and Test Execution.

Experimental Characterization of Emergent Behavior in Bio-Inspired Swarm Intelligence Algorithms

Bio-inspired swarm metaheuristic algorithms constitute a widely used tool for solving complex optimization problems. However, the experimental characterization of their emergent behavior remains a methodological challenge. This study proposes an experimental framework for characterizing emergent behavior through swarm...

A Novel Binary Hunger Games Search Algorithm with Data-Driven Repair for the Set Covering Problem

The Binary Hunger Games Search metaheuristic is selected because the relative quality of each individual determines its hunger level, which in turn regulates the movement of the population and the influence of the best solution found.