Evolution as Computational Learning
AbstractEvolution is basically a form of learning, where the search happens through variation caused by mutations, recombination and other factors, and (natural) selection is a feedback mechanism. There has been much recent work in understanding evolution through a computational lens. One of the fundamental building blocks of life is circuits where the production of protein is controlled by other ones (transcription factors); these circuits are known as transcription networks.
Mathematical models of transcription networks have been proposed using continuous-time Markov processes. The focus of the project is to use these models to understand the expressive power of these networks and whether simple evolutionary algorithms, through suitably guided selection, can result in complex expressive patterns. The work will involve both simulations and theory.