NATURAL ALGORITHMS IN DIGITAL FILTER DESIGN

Digital filters are an important part of Digital Signal Processing (DSP), which plays vital roles within the modern world, but their design is a complex task requiring a great deal of specialised knowledge. An analysis of this design process is presented, which identifies opportunities for the application of optimisation. The Genetic Algorithm (GA) and Simulated Annealing are problem-independent and increasingly popular optimisation techniques. They do not require detailed prior knowledge of the nature of a problem, and are unaffected by a discontinuous search space, unlike traditional methods such as calculus and hill-climbing. Potential applications of these techniques to the filter design process are discussed, and presented with practical results. Investigations into the design of Frequency Sampling (FS) Finite Impulse Response (FIR) filters using a hybrid GA/hill-climber proved especially successful, improving on published results. An analysis of the search space for FS filters provided useful information on the performance of the optimisation technique. The ability of the GA to trade off a filter's performance with respect to several design criteria simultaneously, without intervention by the designer, is also investigated. Methods of simplifying the design process by using this technique are presented, together with an analysis of the difficulty of the non-linear FIR filter design problem from a GA perspective. This gave an insight into the fundamental nature of the optimisation problem, and also suggested future improvements. The results gained from these investigations allowed the framework for a potential 'intelligent' filter design system to be proposed, in which embedded expert knowledge, Artificial Intelligence techniques and traditional design methods work together. This could deliver a single tool capable of designing a wide range of filters with minimal human intervention, and of proposing solutions to incomplete problems. It could also provide the basis for the development of tools for other areas of DSP system design

Heuristic Approaches to Solve the Frequency Assignment Problem

The frequency assignment problem is a computationally hard problem with many applications including the mobile telephone industry and tactical communications. The problem may be modelled mathematically as a T-colouring problem for an undirected weighted graph; it is required to assign to each vertex a value from a given set such that for each edge the difference in absolute value between the values at the corresponding vertices is greater than or equal to the weight of the edge. This problem was solved using novel and existing metaheuristic algorithms and their relative successes were compared. Early work of this thesis used greedy, steepest descent and backtracking algorithms as a means of investigating the factors which influence the performance of an algorithm (selection of frequency, ordering of variables, provision of an incremental objective function). Later simulated annealing, tabu search and divide and conquer techniques were used and the results compared. A novel divide and conquer technique incorporating metaheuristics is described and results using test data based on real problems is presented. The divide and conquer technique (with either tabu search or simulated annealing) was found to improve significantly upon the corresponding metaheuristic when implemented alone and acting on non-trivial scenarios. The results were significant and consistent. The divide and conquer (with simulated annealing) algorithm in particular was shown to be robust and efficient in its solution of the frequency assignment problems presented. The results presented in this thesis consistently out-perform those obtained by the Defence, Evaluation and Research Agency, Malvern. In addition this method lends itself to parallelisation since the problem is broken into smaller independent parts. The divide and conquer algorithm does not exploit knowledge of the constraint network and should be applicable to a number of different problem domains. Algorithms capable of solving the frequency assignment problem most effectively will become valuable as demand for the electromagnetic spectrum continues to grow

Algoritmos evolutivos avanzados como soporte del proceso productivo

El mundo de los negocios actuales está sufriendo muchos cambios, ya no basta con generar reportes y realizar una correcta planificación. Se deben incluir herramientas de optimización para crear soluciones de negocios adaptativas como por ejemplo para límites de créditos, precios y descuentos, y scheduling. Esto redundará en beneficios para la empresa ya sea en la disponibilidad de tecnología de avanzada como también en la disminución de los costos asociados a la toma de decisiones óptimas, también incrementará la capacidad para aprender de experiencias previas y para adaptar a cambios en el mercado. En estos últimos años se han realizados muchos estudios de investigación respecto de la aplicación de las técnicas de computación evolutiva para la solución de problemas de scheduling. La principal ventaja de las técnicas evolutivas es su habilidad para proveer buenas soluciones a problemas extremadamente complejos usando tiempos razonables. En este trabajo se hace un revisión de las clases y características de algoritmos evolutivos así como también algunas mejoras introducidas a los mismos. Entre estas últimas se pueden incluir múltiple crossover, multiplicidad de padres y prevención de incesto. Asimismo se presentan algunas variantes de algoritmos evolutivos planteados para la resolución de un problema particular de scheduling como lo es el problema de job shop scheduling.Facultad de Informátic