The ability to visualize a solution space can be very beneficial, and it is generally accepted that the objective of visualization is to aid researchers in gathering insight. However, insight cannot be gathered effectively if the source data is misrepresented. This dissertation begins by demonstrating that the adaptive landscape visualization in widespread usage frequently misrepresents the neighborhood structure of genotypic space and, consequently, will mislead users about the manner in which solution space is traversed by the genetic algorithm. Bernhard Riemann, the father of topology, explicitly noted that a measurement of the distance between entities should represent the manner in which one can be brought towards the other. Thus, the commonly used Hamming distance, for example, is not representative of traversals of genotypic space by the genetic algorithm – a representative measure must include consideration for both mutation and recombination. This dissertation separately explores the properties that mutational and recombinational distances should have, and ultimately establishes a measure that is representative of the traversals made by both operators simultaneously. It follows that these measures can be used to enhance the adaptive landscape, by minimizing the discrepancy between the interpoint distances in genotypic space and the interpoint distances in the two-dimensional representation from which the landscape is extruded. This research also establishes a methodology for evaluating measures defining neighbourhood structures that are purportedly representative of traversals of genotypic space, by comparing them against an empirically generated norm. Through this approach it is conclusively demonstrated that the Hamming distance between genotypes is less representative than the proposed measures, and should not be used to define the neighbourhood structure from which visualizations would be constructed. While the proposed measures do not distort the data or otherwise mislead the user, they do require a significant computational expense. Fortunately, the choice to use these measures is always made at the discretion of the user, with additional costs incurred when accuracy and representativity are of paramount importance. These measures will ultimately find further application in population diversity measurement, cluster analysis, and any other task where the representativity of the neighborhood structure of the genotypic space is vital.

Spectral unmixing produces spatial abundance maps of endmembers or ‘pure’ materials using sub-pixel scale decomposition. It is particularly well suited to extracting a greater portion of the rich information content in hyperspectral data in support of real-world issues such as mineral exploration, resource management, agriculture and food security, pollution detection, and climate change. However, illumination or shading effects, signature variability, and the noise are problematic. The Least Square (LS) based spectral unmixing technique such as Non-Negative Sum Less or Equal to One (NNSLO) depends on “shade” endmembers to deal with the amplitude errors. Furthermore, the LS-based method does not consider amplitude errors in abundance constraint calculations, thus, often leads to abundance errors. The Spectral Angle Constraint (SAC) reduces the amplitude errors, but the abundance errors remain because of using fully constrained condition. In this study, a Genetic Algorithm (GA) was adapted to resolve these issues using a series of iterative computations based on the Darwinian strategy of ‘survival of the fittest’ to improve the accuracy of abundance estimates. The developed GA uses a Spectral Angle Mapper (SAM) based fitness function to calculate abundances by satisfying a SAC-based weakly constrained condition. This was validated using two hyperspectral data sets: (i) a simulated hyperspectral dataset with embedded noise and illumination effects and (ii) AVIRIS data acquired over Cuprite, Nevada, USA. Results showed that the new GA-based unmixing method improved the abundance estimation accuracies and was less sensitive to illumination effects and noise compared to existing spectral unmixing methods, such as the SAC and NNSLO. In case of synthetic data, the GA increased the average index of agreement between true and estimated abundances by 19.83% and 30.10% compared to the SAC and the NNSLO, respectively. Furthermore, in case of real data, GA improved the overall accuracy by 43.1% and 9.4% compared to the SAC and NNSLO, respectively. / xvi, 85 leaves : ill. (chiefly col.) ; 29 cm

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The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.

This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed.

This thesis describes research into the development of a Computer Aided Design (CAD) tool that uses a Genetic Algorithm (GA) to generate and evolve original design concepts through human interaction. CAD technologies are firmly established in the later stages of design, and include many applications of Evolutionary Algorithms (EAs). The use of EAs as generative and search tools for conceptual design is less evident in fields other than abstract art, architecture and styling. This research gains its originality in aiming to assist designers early in the design process, by creating and evolving aesthetically interesting forms (objects). The integration of GA software with a solid modelling system has enabled the development of a prototype `Evolutionary Form Design' (EFD) system. Objects are defined using a genetic data structure and constructed from various geometric primitives and combinations of Boolean operators. The primitives interact in ways that are not easily predicted, often creating novel shapes that are unlikely to have been discovered through conventional means. Edge blending further adds to objects' complexity and visual appeal. Populations of objects are subjected to a `selective breeding' programme, directed through the user's allocation of scores, and may also be guided by simple geometric targets. These factors determine which objects are `fittest' and most likely to parent a new, hopefully improved generation of objects. The challenge has been to turn the concept into a genuinely useful tool, ensuring that desirable features are reproduced in subsequent populations. The key to achieving this is the way objects are recombined during reproduction. Work has included developing 4 novel routine for grouping the individual primitives that form objects using a Teamforming algorithm. Innovative, aesthetically interesting forms can be evolved intuitively and efficiently, providing inspiration and the initial models for original design concepts. Examples are given where the system'is used by undergraduates to generate seating designs, and by the author, to create virtual sculptures and a range of consumer product concepts.

Jitter and noise on package and printed circuit board interconnects are limiting factors in the performance of high speed digital channels. The simultaneous switching noise (SSN) induced by the return path discontinuities (RPDs) is a major source of noise and jitter on the signal interconnects of these channels. Therefore, optimal design of the power delivery network (PDN) is required to reduce SSN induced noise and jitter and improve the performance of high speed channels. The design of PDN is done in frequency domain whereas jitter and noise are time domain events. As a result, multiple iterations between frequency domain design of PDN and time domain analysis of noise and jitter are required before a design is taped out. A new methodology to correlate PDN impedance with jitter and voltage margin is presented in this thesis. Using this methodology, it would be possible to estimate jitter and noise from the PDN impedance and reduce the iterations involved in freezing the PDN design. The SSN induced at a given RPD is proportional to the PDN impedance at that RPD. As a result, the jitter and the noise can be correlated to the PDN impedance. The PDN impedance is a function of frequency and has alternate local minima and local maxima at resonances and anti-resonances respectively. The anti-resonances in the PDN impedance at the RPD cause significant increase in the insertion loss of signal whose return current is disrupted at that RPD. The increase in the insertion loss attenuates significant harmonics of the signal degrading its rise/fall times and voltage levels. This results in reduction of timing and voltage margins of the signal. Thus, based on the insertion loss profile and harmonic content of the signal, an estimate of jitter and noise on the signal can be made. Passive test vehicles consisting of microstrips with RPDs have been designed and fabricated to demonstrate the proof of concept through both simulations and measurements. Suitable placement of decoupling capacitors is suggested to reduce the PDN impedance below the target impedance and to minimize coupling between two noise ports on the PDN. Genetic algorithm to optimize the selection and placement of decoupling capacitors has been implemented. The efficacy of the algorithm has been demonstrated by testing it on a power delivery networks consisting of a simple power/ground plane pair.

Corrigenda pasted onto back end-paper. / Bibliography: leaves 340-355. / xxvi, 380 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is to develop a mathematical model for the optimum design of pressure irrigation systems. Section I deals with models in which a fixed layout for the piping systems is considered and the enumeration approach is utilised and Section II considers models in which the piping layout is not fixed and the genetic algorithm is utilised as a relatively new approach to optimisation problems. / Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 1996

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Genetic algorithms; planform; optimization; micro aerial vehicle. Includes bibliographical references (p.71-73).