Browsing Computer Science by Title
Now showing items 120 of 104

Adaptive Locally Constrained Genetic Algorithm For LeastCost Water Distribution Network Design
(IWA Publishing, 2014)This paper describes the development of an adaptive locally constrained genetic algorithm (ALCOGA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used ... 
An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design
(Elsevier, 20150207)Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing ... 
Assessment and optimisation of STCA performance: Using the Pareto optimal receiver operating characteristic
(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 tradeoff between ... 
Automated construction of evolutionary algorithm operators for the biobjective water distribution network design problem using a genetic programming based hyperheuristic approach
(IWA Publishing for IAHRIWAIAHS Joint Committee on Hydroinformatics, 2014)The water distribution network (WDN) design problem is primarily concerned with finding the optimal pipe sizes that provide the best service for minimal cost; a problem of continuing importance both in the UK and ... 
The Bayesian Decision Tree Technique with a Sweeping Strategy
(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
(IEEE, 2004)Reasoning from data in practical problems is frequently hampered by missing observations. Mixture models provide a powerful general semiparametric method for modelling densities and have close links to radial basis function ... 
A Bayesian Framework for Active Learning
(Institute of Electrical and Electronics Engineers (IEEE), 2010)We describe a Bayesian framework for active learning for nonseparable 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
(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. ... 
Bayesian unsupervised learning with multiple data types
(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 nonstationary mixing
(2000)Blind source separation attempts to recover independent sources which have been linearly mixed to produce observations. We consider blind source separation with nonstationary mixing, but stationary sources. The linear ... 
Cardinality constrained portfolio optimisation
(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
(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 tradeoff ... 
Computing with confidence: a Bayesian approach
(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
(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 decisiontheoretic ... 
Confident interpretation of Bayesian decision tree ensembles for clinical applications
(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 safetycritical applications such as medical diagnostics. The interpretability ... 
Constructing constrainedversion of magic squares using selection hyperheuristics
(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 TraitBased Particle Swarm Optimisation (CTBPSO)
(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
(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 industrysponsored project that aims to use the computational speed of CA ... 
Distance Metric Learning with Eigenvalue Optimization
(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 DMLeig which is shown to ...