Titlebook: Research and Development in Intelligent Systems XX; Proceedings of AI200 Frans Coenen,Alun Preece,Ann Macintosh Conference proceedings 2004

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Adaptive Mutation Using Statistics Mechanism for Genetic Algorithmssignificant effect on the performance of genetic search. In this paper, a statistics-based adaptive non-uniform mutation (SANUM) is presented within which the probability that each gene will subject to mutation is learnt adaptively over time and over the loci. As a search algorithm based on mechanis

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An Approximate Algorithm for Reverse Engineering of Multi-layer Perceptronsm is a procedure for reconstructing weights of a single linear threshold unit. We prove that the approximation error, measured as the distance between the original and the reconstructed weights, is dropping exponentially fast with the number of queries. The procedure is combined with a labelling str

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A Theoretical Framework for Tradeoff Generation using Soft Constraints configurators, that reason about user preferences . It has been reported how tradeoffs can be modeled as additional constraints. This paper presents a formal framework for tradeoff generation based on the semiring approach to soft constraints. In particular, user preferences and tradeoffs are repre

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Maximum Partial Assignments for Over-Constrained Problemsearch for maximal partial assignments which violate no constraints over assigned variables. We develop a branch-and-bound algorithm which interleaves arc consistency maintenance with reasoning about unassigned variables. We show that the unassigned variables make it difficult to find effective lower

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Escaping Local Optima in Multi-Agent Oriented Constraint Satisfactiony, to improve the overall solution. Our approach, FeReRA, is based on the fact that an agent’s local best performance does not necessarily contribute to the system’s best performance. Thus, agents may be rewarded for improving the system’s performance and penalised for not contributing towards a bet

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Strategies for Partitioning Data in Association Rule Miningor all those sets of attributes which occur often enough to be of possible interest. We have previously described methods we have developed that approach the problem by first constructing a tree (the .-tree) that contains a record of all the relevant information in the database and a partial computa

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