Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort

18.12.14 KB. Ok to add published version to spiral, OA pape

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Synchrotron X-ray tomography reveals the evolving internal morphology of a multi-phase soft solid, ice cream, enabling time dependent quantitation.

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The microstructural patterns formed during liquid to solid phase transformations control 16 the properties of a wide range of materials. We developed a novel methodology that allows in situ 17 quantification of the microstructures formed during solidification of high temperature advanced 18 alloys. The patterns formed are captured in 4D (3D plus time) using a methodology which exploits 19 three separate advances: a bespoke high temperature environment cell; the development of high X-ray 20 contrast alloys; and a novel environmental encapsulation system. This methodology is demonstrated 21 on Ni, Fe, and Co advanced alloy systems, revealing dendritic pattern formation. We present detailed 22 quantification of microstructural pattern evolution in a novel high attenuation contrast Co-Hf alloy, 23 including microstructural patterning and dendrite tip velocity. The images are quantified to provide 24 4D experimental data of growth and coarsening mechanisms in Co alloys, which are used for a range 25 of applications from energy to aerospace. 26 27

In this paper we describe a generic methodology to create an "optimal" feature extraction pre-processing stage for pattern classification. Our aim is to map the input data into a new, onedimensional feature space in which separability is maximized under a simple thresholding classification. We have used multiobjective genetic programming with Pareto strength-based ranking to bias the selection procedure. The methodology is applied to the edge detection problem in image processing; we make quantitative comparison with the pre-processing stages of the well-known Canny edge detector using synthetic and realworld edge data and conclude that the performance of our evolutionary-based method is much superior to the Canny algorithm based on the criterion of minimum Bayes risk.