Evolutionary multi-objective algorithms for Global Grids. This research investigates alternative software technologies for Grid-based evolutionary multi-objective decision algorithms. By employing the latest adaptive techniques and taking advantage of the low cost Grid infrastructure, new parallel evolutionary systems will be developed that can rapidly supply robust solutions to complex problems. This project will further develop an understanding of scaling issues in parallel evolutionary syste ....Evolutionary multi-objective algorithms for Global Grids. This research investigates alternative software technologies for Grid-based evolutionary multi-objective decision algorithms. By employing the latest adaptive techniques and taking advantage of the low cost Grid infrastructure, new parallel evolutionary systems will be developed that can rapidly supply robust solutions to complex problems. This project will further develop an understanding of scaling issues in parallel evolutionary systems and pave the way for even more widespread application of evolutionary techniques for large scale, data-intensive applications in science and industry.Read moreRead less
Realising the promise of neural networks for practical optimisation: improving their efficiency and effectivess through chaotic dynamics and hardware implementation. Combinatorial optimisation problems such as transportation routing and assembly-line scheduling are critical to the efficiency of many industries, but their combinatorial explosion makes rapid solution difficult. Neural networks (NNs) hold much potential for rapid solution though hardware implementation, but we need to improve the q ....Realising the promise of neural networks for practical optimisation: improving their efficiency and effectivess through chaotic dynamics and hardware implementation. Combinatorial optimisation problems such as transportation routing and assembly-line scheduling are critical to the efficiency of many industries, but their combinatorial explosion makes rapid solution difficult. Neural networks (NNs) hold much potential for rapid solution though hardware implementation, but we need to improve the quality of their solutions before developing hardware. We have previously shown that the rich dynamics of chaos can improve the efficiency and effectiveness of NNs. We aim to develop new chaotic NN models, rigorously evaluate them on industrially significant problems such as those arising in manufacturing, logistics and telecommunications, and demonstrate their speed through hardware acceleration.Read moreRead less