Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Optimisation of a weightless neural network using particle swarms

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This thesis is concerned with the design of weightless neural networks, which decompose a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. In this thesis the application of a population-based stochastic optimisation technique, known as Particle Swarm Optimisation (PSO), to the optimisation of the connectivity pattern of such “n-tuple” classifiers is explored. The research was aimed at improving the discriminating power of the classifier in recognising handwritten characters by exploiting more efficient learning strategies. The proposed "learning" scheme searches for ‘good’ input connections of the n-tuples in the solution space and shrinks the search area step by step. It refines its search by attracting the particles to positions with good solutions in an iterative manner. Every iteration the performance or fitness of each input connection is evaluated, so a reward and punishment based fitness function was modelled for the task. The original PSO was refined by combining it with other bio-inspired approaches like Self-Organized Criticality and Nearest Neighbour Interactions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The Genetic Algorithm (GA) has been shown to be accomplishing the same goals as the PSO, so the performances and convergence properties of the GA were compared against the PSO to optimise input connections. Experiments were conducted to evaluate the proposed methods by applying the trained classifiers to recognise handprinted digits from a widely used database. Results revealed the superiority of the particle swarm optimised training for the n-tuples over other algorithms including the GA. Low particle velocity in PSO was favourable for exploring more areas in the solution space and resulted in better recognition rates. Use of hybridisation was helpful and one of the versions of the hybrid PSO was found to be the best performing algorithm in finding the optimum set of input maps for the n-tuple network

Simulation, optimization and instrumentation of agricultural biogas plants

During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive. The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants. The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes

PSO-based coevolutionary Game Learning

Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950’s. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoners Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD – the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.Dissertation (MSc)--University of Pretoria, 2005.Computer Scienceunrestricte