Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

publisher: Elsevier articletitle: Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks journaltitle: Ad Hoc Networks articlelink: http://dx.doi.org/10.1016/j.adhoc.2016.08.005 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Inexact Bayesian point pattern matching for linear transformations

PublishedArticleWe introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-to-one correspondence between the point sets and the presence of noise. The algorithm is itself inexact; we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm.This work was supported by the University of Exeter’s Bridging the Gaps initiative, which was funded by EPSRC award EP/I001433/1 and the collaboration was formed through the Exeter Imaging Network

Variational Bayesian tracking: whole track convergence for large scale ecological video monitoring

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Conference paper: IEEE International Joint Conference on Neural Networks (IJCNN), 4-9 Aug. 2013, Dallas, Texas, USA.Variational Bayesian approximations offer a computationally fast alternative to numerical approximations for Bayesian inference. We examine variational Bayesian methods for filtering and smoothing continuous hidden Markov models, in particular those with sharply-peaked, nonlinear observations densities. We show that, by making variational updates in the correct order, robust convergence to the tracked state may be achieved. We apply the whole track convergence algorithm to tracking wild crickets in video streams and describe how animals may be identified from the characteristics of their tracks. We also show how identifying alphanumeric tags may be read under poor lighting conditions

Redesign of Industrial Apparatus using Multi-Objective Bayesian Optimisation

This is the author accepted manuscriptIntroduction. Design optimisation using Computational Fluid Dynamics (CFD) often requires extremising multiple (and often conflicting) objectives simultaneously. For instance, a heat exchanger design will require maximising the heat transfer across the media, while minimising the pressure drop across the apparatus. In such cases, usually there is no unique solution, but a range of solutions trading off between the objectives. The set of solutions optimally trading off the objectives are known as the Pareto set, and in practice only an approximation of the set may be achieved. Multi-Objective Evolutionary Algorithms (MOEAs) are known to perform well in estimating the optimal Pareto set. However, they require thousands of function evaluations, which is impractical with computationally expensive simulations. An alternative is to use Multi-Objective Bayesian Optimisation (MOBO) method that has been proved to be an effective approach with limited budget on function evaluations [1]. In this work, we illustrate a newly developed MOBO framework in [1] with OpenFOAM 2.3.1 to locate a good estimation of the optimal Pareto set for a range of industrial cases.This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant (reference number: EP/M017915/1) for the CEMPS, University of Exeter, UK

Application of multi-objective Bayesian shape optimisation to a sharp-heeled Kaplan draft tube

The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian methodology. The chosen design objectives were to maximise pressure recovery, and minimise wall-frictional losses along the geometry. The design variables were chosen to explore potential new designs, using a series of subdivision-curves and splines on the inflow cone, outer-heel, and diffuser. The optimisation run was performed under part-load for the Kaplan turbine. The design with the lowest energy-loss identified on the Pareto-front was found to have a straight tapered diffuser, chamfered heel, and a convex inflow cone. Analysis of the performance quantities showed the typically used energy-loss factor and pressure recovery were highly correlated in cases of constant outflow cross-sections, and therefore unsuitable for use of multi-objective optimisation. Finally, a number of designs were tested over a range of discharges. From this it was found that reducing the heel size increased the efficiency over a wider operating range

Multi-objective Bayesian optimisation using an exploitative attainment front acquisition function

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordEfficient methods for optimising expensive black-box problems with multiple objectives can often themselves become prohibitively expensive as the number of objectives is increased. We propose an infill criterion based on the distance to the summary attainment front which does not rely on the expensive hypervolume or expected improvement computations, which are the principal causes of poor dimensional scaling in current stateof-the-art approaches. By evaluating performance on the wellknown Walking Fish Group problem set, we show that our method delivers similar performance to the current state-of-theart. We further show that methods based on surrogate mean predictions are more often than not superior to the widely used expected improvement, suggesting that the additional exploration produced by accounting for the uncertainty in the surrogate’s prediction of the optimisation landscape is often unnecessary and does not aid convergence towards the Pareto fron

Optimising diversity in classifier ensembles of classification trees

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record24th International Conference on the Applications of Evolutionary Computation (Part of EvoStar 2021). EvoApplications 2021, Virtual Event, 7 - 9 April 2021Ensembles of predictors have been generally found to have better performance than single predictors. Although diversity is widely thought to be an important factor in building successful ensembles, there have been contradictory results in the literature regarding the influence of diversity on the generalisation error. Fundamental to this may be the way diversity itself is defined. We present two new diversity measures, based on the idea of ambiguity, obtained from the bias-variance decomposition by using the cross-entropy error or the hinge-loss. If random sampling is used to select patterns on which ensemble members are trained, we find that generalisation error is negatively correlated with diversity at high sampling rates; conversely generalisation error is positively correlated with diversity when the sampling rate is low and the diversity high. We use evolutionary optimisers to select the subsets of patterns for predictor training by maximising these diversity measures on training data. Evaluation of their generalisation performance on a range of classification datasets from the literature shows that the ensembles obtained by maximising the cross-entropy diversity measure generalise well, enhancing the performance of small ensembles. Contrary to expectation, we find that there is no correlation between whether a pattern is selected and its proximity to the decision boundary

Towards Many-objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

PPSN 2016: 14th International Conference on Parallel Problem Solving from Nature, 17-21 September 2016, Edinburgh, ScotlandThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We in- corporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.This work was funded under EPSRC grant EP/K000519/1

Asynchronous ε-greedy Bayesian optimisation

This is the author accepted manuscript. The final version is available from ML Research Press via the link in this recordUAI2021: 37th Conference on Uncertainty in Artificial Intelligence, 27 - 30 July 2021. OnlineBatch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource utilisation. To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous ε-Greedy Global Search) that combines greedy search, exploiting the surrogate's mean prediction, with Thompson sampling and random selection from the approximate Pareto set describing the trade-off between exploitation (surrogate mean prediction) and exploration (surrogate posterior variance). We demonstrate empirically the efficacy of AEGiS on synthetic benchmark problems, meta-surrogate hyperparameter tuning problems and real-world problems, showing that AEGiS generally outperforms existing methods for asynchronous BO. When a single worker is available performance is no worse than BO using expected improvement.Innovate U

How Bayesian Should Bayesian Optimisation Be?

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordBayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood. However, this fails to account for uncertainty in the hyperparameters themselves, leading to overconfident model predictions. This uncertainty can be accounted for by taking the Bayesian approach of marginalising out the model hyperparameters. We investigate whether a fully-Bayesian treatment of the Gaussian process hyperparameters in BO (FBBO) leads to improved optimisation performance. Since an analytic approach is intractable, we compare FBBO using three approximate inference schemes to the maximum likelihood approach, using the Expected Improvement (EI) and Upper Confidence Bound (UCB) acquisition functions paired with ARD and isotropic Matern kernels, across 15 well-known benchmark problems for 4 observational noise settings. FBBO using EI with an ARD kernel leads to the best performance in the noise-free setting, with much less difference between combinations of BO components when the noise is increased. FBBO leads to over-exploration with UCB, but is not detrimental with EI. Therefore, we recommend that FBBO using EI with an ARD kernel as the default choice for BO.Innovate U