@inbook {IOPORT.05858257,
    author       = {Lygoe, Robert J. and Cary, Mark and Fleming, Peter J.},
    title        = {A many-objective optimisation decision-making process applied to automotive Diesel engine calibration.},
    year         = {2010},
    booktitle    = {Simulated evolution and learning. 8th international conference, SEAL 2010, Kanpur, India, December 1--4, 2010. Proceedings},
    isbn         = {978-3-642-17297-7},
    pages        = {638-646},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-17298-4_72},
    abstract     = {Summary: A novel process has been developed for reducing complexity in real-world, high-dimensional, multi-objective optimisation problems. This approach relies on being able to identify and exploit local harmony between objectives to reduce dimensionality. To achieve this, a systematic and modular process has been designed to cluster the Pareto-optimal front and apply a rule-based Principal Component Analysis including preference articulation for potential objective reduction. This many-objective optimisation decision-making process is demonstrated on a real-world, automotive diesel engine calibration optimisation problem comprising six objectives. The complexity reduction process resulted in three- and four-objective sub-problems. In the former, a significant improvement was achieved in one of the retained objectives at very little cost to the others.},
    identifier   = {05858257},
}