The synthesis of abiotic life-like behaviour in complex chemical systems is one of the great scientific challenges in today’s research environment. Very often in this type of design, the parameter space is so large and the system so complex that analytical, rational design techniques are extremely difficult to manage, and more often than not, unavailable altogether. Machine learning methods have found many applications in the realm of design and manufacture and the research described in this thesis describes the application of these tools towards the development of pre-specified chemical functionality in complex systems. A detailed description of the ‘Evolutionary Engine’ built with this sort of design in mind is given, including preliminary investigations into coupling this engine to a ‘real life’ chemical reactor array. Studies are performed on a range of complex systems, including benchmark problems based on cellular automata, and, for the first time in this domain, on real world problems in self-organised scanning probe microscopy. Given a target behaviour of the system in question, usually represented by a series of patterns in a 2D image, it is shown that parameters can be ‘reverse engineered’ through a sophisticated combination of machine learning techniques and image analysis methods, such that the target behaviour/pattern can be faithfully reproduced. Finally, techniques for the approximation of a complex system and its associated fitness function are explored, giving rise to a dramatic decrease in computation time with little compromise to the quality of results.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:519354 |
| Date | January 2010 |
| Creators | Siepmann, Peter A. |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/11135/ |
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