Full Elite Sets for Multi-Objective Optimisation

Copyright © 2002 Springer. The final publication is available at link.springer.com5th International Conference on Adaptive Computing in Design and Manufacture (ACDM 2002), Exeter, UK, 16-18 April, 2002Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of frontal estimates found using the full archive is illustrated on test problems

Assuring the model evolution of protocol software specifications by regression testing process improvement

A preliminary version of this paper has been presented at the 10th International Conference on Quality Software (QSIC 2010).Model-based testing helps test engineers automate their testing tasks so that they are more cost-effective. When the model is changed because of the evolution of the specification, it is important to maintain the test suites up to date for regression testing. A complete regeneration of the whole test suite from the new model, although inefficient, is still frequently used in the industry, including Microsoft. To handle specification evolution effectively, we propose a test case reusability analysis technique to identify reusable test cases of the original test suite based on graph analysis. We also develop a test suite augmentation technique to generate new test cases to cover the change-related parts of the new model. The experiment on four large protocol document testing projects shows that our technique can successfully identify a high percentage of reusable test cases and generate low-redundancy new test cases. When compared with a complete regeneration of the whole test suite, our technique significantly reduces regression testing time while maintaining the stability of requirement coverage over the evolution of requirements specifications. Copyright © 2011 John Wiley & Sons, Ltd.link_to_subscribed_fulltex

Visualising many-objective populations

Copyright © 2012 ACM14th International Conference on Genetic and Evolutionary Computation (GECCO 2012), Philadelphia, USA, 7-11 July 2012Optimisation problems often comprise a large set of objectives, and visualising the set of solutions to a problem can help with understanding them, assisting a decision maker. If the set of objectives is larger than three, visualising solutions to the problem is a difficult task. Techniques for visualising high-dimensional data are often difficult to interpret. Conversely, discarding objectives so that the solutions can be visualised in two or three spatial dimensions results in a loss of potentially important information. We demonstrate four methods for visualising many-objective populations, two of which use the complete set of objectives to present solutions in a clear and intuitive fashion and two that compress the objectives of a population into two dimensions whilst minimising the information that is lost. All of the techniques are illustrated on populations of solutions to optimisation test problems

A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts

Copyright © 2005 Springer Verlag. The final publication is available at link.springer.com3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005. ProceedingsBook title: Evolutionary Multi-Criterion OptimizationIn extending the Particle Swarm Optimisation methodology to multi-objective problems it is unclear how global guides for particles should be selected. Previous work has relied on metric information in objective space, although this is at variance with the notion of dominance which is used to assess the quality of solutions. Here we propose methods based exclusively on dominance for selecting guides from a non-dominated archive. The methods are evaluated on standard test problems and we find that probabilistic selection favouring archival particles that dominate few particles provides good convergence towards and coverage of the Pareto front. We demonstrate that the scheme is robust to changes in objective scaling. We propose and evaluate methods for confining particles to the feasible region, and find that allowing particles to explore regions close to the constraint boundaries is important to ensure convergence to the Pareto front

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Frechet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.Comment: 7 pages, 4 figures; This work has been accepted by the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (IEEE EMBC 2023'). Copyright will be transferred without notice, after which this version may no longer be accessibl