Learning classifier systems (LCSs) are a machine learning technique, which combine reinforcement learning and evolutionary algorithms to evolve a set of classifiers (or rules) for pattern classification tasks. Despite promising performance across a variety of data sets, the performance of LCS is often degraded when data sets of high dimensionality and relatively few instances are encountered, a common occurrence with gene expression data. In this paper, we propose a number of extensions to XCS, a widely used accuracy-based LCS, to tackle such problems. Our model, CoXCS, is a coevolutionary multi-population XCS. Isolated sub-populations evolve a set of classifiers based on a partitioning of the feature space in the data. Modifications to the base XCS framework are introduced including an algorithm to create the match set and a specialized crossover operator. Experimental results show that the accuracy of the proposed model is significantly better than other well-known classifiers when the ratio of data features to samples is extremely large.

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks, on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate neural networks into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial neural network as the classifier’s action, we obtain a more compact population size, better generalization, and the same or better accuracy, while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant neural network ensemble. NCL is shown to improve the generalization of the ensemble. I.

There are many people I would like to thank for their support. Principally I thank my family, Sue, Nick, Katie and William, and my Supervisor Larry Bull. All have shown tolerance, forbearance, support and inspiration well beyond the call of duty. In addition Dave Wyatt and Praminda Caleb-Solly have been very knowledgeable, approachable and helpful, I owe them a lot. The UWE LCSG has been a key source of ideas, knowledge and inspiration. Books and papers cannot provide such a level of understanding and debate about fundamental issues. Amongst the key players Larry Bull, Alwyn Barry, Dave Wyatt, Matt Studley, Chris Stone, Tony Pipe, Brian Carse and Rob Smith have always provided difficult and thought provoking questions, and sometimes even answers. Lastly thanks to Paul Lewis, Joe Mackenzie both of BT, Terry Fogarty, and Roger Miles who in there own ways facilitated the move from being a competent project manager to entering the (from Huxley) “brave new world that hath such people in’t ” of evolutionary and neural computing. This thesis investigates a hybrid of evolutionary computing and neural computing which long has been a goal of machine learning. X-NCS is a neural and hence a more complex version of XCS (Wilson 1995), the pre-eminent accuracy based Learning Classifier System (LCS) (Holland, 1986). XCS differs from other

condition-action rules based on feature space partitions