My Research

I am conducting research on evolutionary algorithms representations' properties to understand how and why these algorithms either work or fail. To do so, I have conducted in-depth theoretical studies (e.g., neutrality, locality), and relatively recently, I have also started using real-world problems.


I am also very interested in Monte Carlo Tree Search, which is widely applicable in any domain where simulation and statistical modelling can be used to predict outcomes, such as behavioural modelling, decision support, to mention a few examples.


What Evolutionary Algorithms (EAs) and Monte Carlo Tree Search (MCTS) have in common is that they are easily transferable from one application to another.


To test these ideas, I have used well-known and well-defined benchmark problems from these two areas as well as real-world problems (e.g., intelligent charging of electric vehicles, optimisation of code, automated design of game controllers).


See my list of publications.


Students interested in studying a Ph.D. in any of these domains are encouraged to contact me.