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.

Genetic Algorithms (GAs) are search algorithms inspired by genetics and natural selection, and have been used to solve difficult problems in many disciplines, including modelling, control systems and automation. GAs are generally able to find good solutions in reasonable time, however as they are applied to larger and harder problems they are very demanding in terms of computation time and memory. The Internet is the most powerful parallel and distributed computation environment in the world, and the idle cycles and memories of computers on the Internet have been increasingly recognized as a huge untapped source of computation power. By combining Internet computing and GAs, this dissertation provides a framework for Internet-based parallel and distributed GAs that gives scientists and engineers an easy and affordable way to solve hard real world problems. Developing parallel computation applications on the Internet is quite unlike developing applications in traditional parallel computation environments, such as multiprocessor systems and clusters. This is because the Internet is different in many respects, such as communication overhead, heterogeneity and volatility. To develop an Internet-based GA, we need to understand the implication of these differences. For this purpose, a convergence model for heterogenous and volatile networks is presented and used in experiments that study GA performance and robustness in Internet-like scenarios. The main outcome of this research is an Internet-based distributed GA framework called G2DGA. G2DGA is an island model distributed GA, which can provide support for big populations needed to solve many real world problems. G2DGA uses a novel hybrid peer-to-peer (P2P) design with island node activity coordinated by supervisor nodes that offer a global overview of the GA search state. Compared to client/server approaches, the P2P architecture improves scalability and fault tolerance by allowing direct communication between the islands and avoiding single-point-of-failure situations. One of the defining characteristics of Internet computing is the dynamics and volatility of the environment, and a parallel and distributed GA that does not adapt to its environment cannot use the available resources efficiently. Two novel adaptive methods are investigated. The first method is migration topology adaptation, which uses clustering on elite individuals from each island to rebuild the migration topology. Experiments with the migration topology adapter show that it gives G2DGA better performance than a GA with static migration topology of a similar or larger connectivity level. The second method is population size adaptation, which automatically finds the number of islands and island population sizes needed to solve a given problem efficiently. Experiments on the population size adapter show that it is robust, and compares favourably with the traditional trial-and-error approach in terms of computational effort and solution quality. The scalability and robustness of G2DGA has been extensively tested in network scenarios of varying volatility and heterogeneity. Experiments with up to 60 computers were conducted in computer laboratories, while more complex network scenarios have been studied in an Internet simulator. In the experiments, G2DGA consistently performs as well as, and usually significantly better than, static distributed GAs and the difference grows larger with increased network instability. The results show that G2DGA, by continuously adjusting the migration policy and the population size, can detect and make efficient use of idle cycles donated over volatile Internet connections. To demonstrate that G2DGA can be used to implement and solve real world problems, a challenging application in VLSI design was developed and used in the testing of the framework. The application is a multi-layer floorplanner, which uses a novel GA representation and operators based on a slicing structure approach. Its packing quality compares favourably with other multi-layer floorplanners found in the literature. Internet-based distributed GA research is exciting and important since it enables GAs to be applied to problem areas where resource limitations make traditional approaches unworkable. G2DGA provides a scalable and robust Internet-based distributed GA framework that can serve as a foundation for future work in the field.

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).

Thesis (M.S.)--Ohio University, June, 1999. / Title from PDF t.p.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 152-162). Also available via the Internet.