Many problems within the biological sciences, such as DNA sequencing, protein structure prediction, and molecular docking, are being approached computationally. These problems require sophisticated solution methods that understand the complex natures of biological domains. Traditionally, such solution methods are problem specific, but recent advances in generic problem-solvers furnish hope for a new breed of computational tools. The challenge is to develop methods that can automatically learn or acquire an understanding of a complex problem domain. Estimation of Distribution Algorithms (EDAs) are generic search methods that use statistical models to learn the structure of a problem domain. EDAs have been successfully applied to many difficult search problems, such as circuit design, optimizing Ising spin glasses, and various scheduling tasks. However, current EDAs contain ad hoc limitations that reduce their capacity to solve hard problems. This dissertation presents a new EDA method, the Markovian Learning Estimation of Distribution Algorithm (MARLEDA), that employs a Markov random field model. The model is learned in a novel way that overcomes previous ad hoc limitations. MARLEDA is shown to perform well on standard benchmark search tasks. A multiobjective extension of MARLEDA is developed for use in predicting the secondary structure of RNA molecules. The extension is shown to produce high-quality predictions in comparison with several contemporary methods, laying the groundwork for a new computational tool for RNA researchers.

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Probabilistic bisimulation is a widely studied equivalence relation for stochastic systems. However, it requires the behavior of the states to match on actions with matching labels. This does not allow bisimulation to capture symmetries in the system. In this thesis we define lax probabilistic bisimulation, in which actions are only required to match within given action equivalence classes. We provide a logical characterization and an algorithm for computing this equivalence relation for finite systems. We also specify a metric on states which assigns distance 0 to lax-bisimilar states. We end by examining the use of lax bisimulation for analyzing Markov Decision Processes (MDPs) and show that it corresponds to the notion of a MDP homomorphism, introduced by Ravindran & Barto. Our metric provides an algorithm for generating an approximate MDP homomorphism and provides bounds on the quality of the best control policy that can be computed using this approximation.

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Plasma polymerisation (PP) is an emerging processing technology with immense potential for future industrial applications, which is increasingly being used for the fabrication of functional coatings on polymeric substrates. In this technique, the solid polymeric film is directly deposited onto the substrate surface to create a new surface of very interesting and unique properties. PP utilizes gas phase chemistries in low pressure environment to produce well-defined high quality films in controllable and tunable fashion. A major advantage of this process is that it is an environmental safety technique and strategically superior compared to other thin film deposition techniques such as spin coating and spray coating. In eneral, the quality of the plasma polymer film can be controlled, precisely and reproducible. However, mechanism of the coating under plasma polymerisation is complex and has not yet been completely understood. / Thesis (PhD)--University of South Australia, 1999