Evolutionary multi-objective algorithms for Global Grids

本研究探讨了基于网格的进化多目标决策算法的替代软件技术。通过采用最新的自适应技术和利用低成本的网格基础设施，新的并行进化系统将被开发出来，可以快速为复杂问题提供强大的解决方案。该项目将进一步发展对并行进化系统中缩放问题的理解，并为进化技术在科学和工业中的大规模、数据密集型应用的更广泛应用铺平道路。

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.