Computational steering of a multi-objective evolutionary algorithm for engineering design

The execution process of an evolutionary algorithm typically involves some trial and error. This is due to the difficulty in setting the initial parameters of the algorithm—especially when little is known about the problem domain. This problem is magnified when applied to many-objective optimisation, as care is needed to ensure that the final population of candidate solutions is representative of the trade-off surface. We propose a computational steering system that allows the engineer to interact with the optimisation routine during execution. This interaction can be as simple as monitoring the values of some parameters during the execution process, or could involve altering those parameters to influence the quality of the solutions produced by the optimisation process. The implementation of this steering system should provide the ability to tailor the client to the hardware available, for example providing a lightweight steering and visualisation client for use on a PDA

Distributed decision support systems

Decision support systems are a class of computer based systems that assist in some or all levels of decision making within an organisation. Recently, the growth of data captured that is useful or even critical to the successful running or conclusion of projects in science and industry has been remarkable. Thus, the development of decision support systems that are scalable in terms of the size of data processed. the number of stakeholders, and their geographical span has become of the essence. This thesis identifies the issues in developing distributed decision support systems. Building on that. an architectural style for the development of scalable and extensible software systems is introduced. Subsequently, a framework for the design of distributed decision support systems is developed. This new architectural style is the Resource Oriented Services Architecture (ROSA). It builds on Representational State Transfer (REST), an architectural style that describes the venerable design of the world wide web. An architectural design based on REST revolves around resources, representations, and hyperlinks. \Vhat it lacks is a standardised way to represent computations as resources in a scalable and extensible manner. For systems that cannot be adequately described as a web of documents, this is a shortcoming. ROSA overcomes this by defining a means of representing executable resources in a manner that is consistent with the statelessness and cacheability constraints of REST. The resulting architecture enables the scalability of the system. Additionally, desirable features such as dynamic discovery of resources and extensibility and loose coupling are attained. To illustrate this framework, two new learning algorithms are introduced and implemented as services. The first is a data structure suitable for proximity queries over large datasets of low intrinsic dimension. The other uses a random projection to carry out novelty detection over high dimensional datasets.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of hERG inhibition, solubility, and lipophilicity

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches