An Evolutionary Algorithm Approach to Guiding the Evolution of Self-Organised Nanostructured Systems

Natalio Krasnogor

University of Nottingham

Abstract

Complex systems in chemistry, physics, biology, ecology, economics, computer science and beyond have often been modeled using cellular automata and the closely related technique known as lattice gases. Both approaches are appealing modeling paradigms not least because they allow for a piece-meal specification of the laws that govern a given systems dynamics but also because they are intrinsically distributed tools amenable to computational parallelization. However, due to the complex nature of the underlying processes that are simulated with these methods, it is not always possible to analytically derive specific values for the many model parameters that control their time-space evolution. This problem gets more insidious when the intention is for the simulation to quantitatively match observations made in the laboratory on experiments whose