Research Topics
Computational Intelligence
Computational Intelligence (CI) encompasses the development of bio-inspired computational tools aimed at addressing various applications. It primarily concentrates on Artificial Neural Networks, Fuzzy Logic, and Evolutionary Computation.
My interest lies in the development and application of these tools within engineering contexts, particularly in the area of multi-objective optimization.
Evolutionary Multi-objective Optimization
Optimization problems in real-world scenarios frequently involve conflicting objectives. In such a case, a single solution cannot optimize all the objective functions without prior scalarization.
Performance trade-offs become significant in the selection process when objectives conflict. To comprehend these trade-offs, vector-based optimization essential, as evidenced by the extensive body of literature on evolutionary multi-objective optimization. The primary objective of these algorithms is to accurately and diversely approximate the set of Pareto-optimal solutions, which are defined as those solutions that cannot be improved in one objective without compromising another.
I am particularly interested in utilizing these tools for engineering optimization tasks, including multi-objective robust optimization and multi-objective neuro-evolution.
Evolutionary Robotics and Neuro-control
Evolutionary robotics is a computational framework aimed at autonomously creating robot controllers and other crucial robotic elements, such as mechanics, sensors, and actuators. In this field, robots are evolved to maximize rewards, typically gained through interactions with simulated or real-world environments.
I am particularly interested in developing and utilizing tools for designing neuro controllers, which are artificial neural networks that serve as controllers for robotic tasks. Additionally, I keen on using these tools to explore brain-computer interfaces.