Now showing items 1-20 of 96

  • Adaptive Locally Constrained Genetic Algorithm For Least-Cost Water Distribution Network Design 

    Johns, Matthew B.; Keedwell, Edward; Savic, Dragan (IWA Publishing, 2014)
    This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used ...
  • Assessment and optimisation of STCA performance: Using the Pareto optimal receiver operating characteristic 

    Reckhouse, William; Everson, Richard M.; Fieldsend, Jonathan E.; Bush, David; Arnold, Trevor; Hayward, Richard; Slater, Keith (2008)
    Short Term Conflict Alert (STCA) systems are complex software programs, with many parameters that must be adjusted to achieve best performance. We describe a simple evolutionary algorithm for optimising the trade-off between ...
  • The Bayesian Decision Tree Technique with a Sweeping Strategy 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (2004)
    The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably ...
  • Bayesian estimation and classification with incomplete data using mixture models 

    Zhang, Jufen; Everson, Richard M. (IEEE, 2004)
    Reasoning from data in practical problems is frequently hampered by missing observations. Mixture models provide a powerful general semi-parametric method for modelling densities and have close links to radial basis function ...
  • A Bayesian Framework for Active Learning 

    Fredlund, Richard; Everson, Richard M.; Fieldsend, Jonathan E. (Institute of Electrical and Electronics Engineers (IEEE), 2010)
    We describe a Bayesian framework for active learning for non-separable data, which incorporates a query density to explicitly model how new data is to be sampled. The model makes no assumption of independence between queried ...
  • Bayesian inductively learned modules for safety critical systems 

    Fieldsend, Jonathan E.; Bailey, Trevor C.; Everson, Richard M.; Krzanowski, Wojtek J.; Partridge, Derek; Schetinin, Vitaly (Interface Foundation of North America, Inc., 2003)
    This work examines the use of Bayesian inductively learned software modules for safety critical systems. Central to the safety critical application is the desire to generate confidence measures associated with predictions. ...
  • A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Bailey, Trevor C.; Everson, Richard M.; Hernandez, Adolfo (IOS Press, 2006)
  • Bayesian unsupervised learning with multiple data types 

    Agius, Phaedra; Ying, Yiming; Campbell, Colin (Walter de Gruyter, 2009)
    We propose Bayesian generative models for unsupervised learning with two types of data and an assumed dependency of one type of data on the other. We consider two algorithmic ap- proaches, based on a correspondence model ...
  • Blind source separation for non-stationary mixing 

    Everson, Richard M.; Roberts, Stephen (2000)
    Blind source separation attempts to recover independent sources which have been linearly mixed to produce observations. We consider blind source separation with non-stationary mixing, but stationary sources. The linear ...
  • Cardinality constrained portfolio optimisation 

    Fieldsend, Jonathan E.; Matatko, John; Peng, Ming (Springer Berlin Heidelberg, 2004)
    The traditional quadratic programming approach to portfolio optimisation is difficult to implement when there are cardinality constraints. Recent approaches to resolving this have used heuristic algorithms to search for ...
  • Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (Springer, 2006)
    Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off ...
  • Computing with confidence: a Bayesian approach 

    Partridge, Derek; Fieldsend, Jonathan E.; Krzanowski, Wojtek J.; Bailey, Trevor C.; Everson, Richard M.; Schetinin, Vitaly (2006)
    Bayes’ rule is introduced as a coherent strategy for multiple recomputations of classifier system output, and thus as a basis for assessing the uncertainty associated with a particular system results --- i.e. a basis for ...
  • Confidence in Classification: A Bayesian Approach 

    Krzanowski, Wojtek J.; Bailey, Trevor C.; Partridge, Derek; Fieldsend, Jonathan E.; Everson, Richard M.; Schetinin, Vitaly (Springer Verlag, 2006)
    Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic ...
  • Confident interpretation of Bayesian decision tree ensembles for clinical applications 

    Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Coats, Timothy J.; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C.; Hernandez, Adolfo (Institute of Electrical and Electronics Engineers (IEEE), 2007)
    Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability ...
  • Constructing constrained-version of magic squares using selection hyper-heuristics 

    Kheiri, Ahmed; Özcan, Ender (Oxford University Press for BCS, The Chartered Institute for IT, 2014)
    A square matrix of distinct numbers in which every row, column and both diagonals have the same total is referred to as a magic square. Constructing a magic square of a given order is considered a difficult computational ...
  • Continuous Trait-Based Particle Swarm Optimisation (CTB-PSO) 

    Keedwell, Edward; Morley, Mark; Croft, Darren (Springer Verlag, 2012)
    In natural flocks, individuals are often of the same species, but there exists considerable variation in the traits possessed by each individual. In much the same way as humans display varied levels of aggression, ...
  • Design of a graphical framework for simple prototyping of pluvial flooding cellular automata algorithms 

    Guidolin, Michele; Duncan, Andrew; Keedwell, Edward; Chen, Albert S.; Djordjevic, Slobodan; Savic, Dragan (Centre for Water Systems, University of Exeter, 2011)
    Cellular automata (CA) algorithms can be used for quickly describing models of complex systems using simple rules. CADDIES is a new EPSRC and industry-sponsored project that aims to use the computational speed of CA ...
  • Distance Metric Learning with Eigenvalue Optimization 

    Ying, Yiming; Peng, Li (Microtome Publishing, 2012)
    The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning approach called DML-eig which is shown to ...
  • Dominance Measures for Multi-Objective Simulated Annealing 

    Smith, Kevin I.; Everson, Richard M.; Fieldsend, Jonathan E. (Institute of Electrical and Electronics Engineers (IEEE), 2004)
    Simulated annealing (SA) is a provably convergent optimiser for single-objective (SO) problems. Previously proposed MO extensions have mostly taken the form of an SO SA optimising a composite function of the objectives. ...
  • Dominance-Based Multiobjective Simulated Annealing 

    Smith, Kevin I.; Everson, Richard M.; Fieldsend, Jonathan E.; Murphy, Chris; Misra, Rashmi (Institute of Electrical and Electronics Engineers (IEEE), 2008)
    Simulated annealing is a provably convergent optimizer for single-objective problems. Previously proposed multiobjective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite ...