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Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.


@inproceedings{DeMelo:2007:DPR:1775967.1775976,
author = {De Melo, Vin\’{\i}cius V. and Delbem, Alexandre C. B. and J\’{u}nior, Dorival L. Pinto and Federson, Fernando M.},
title = {Discovering promising regions to help global numerical optimization algorithms},
booktitle = {Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence},
series = {MICAI’07},
year = {2007},
isbn = {3-540-76630-8, 978-3-540-76630-8},
location = {Aguascalientes, Mexico},
pages = {72–82},
numpages = {11},
url = {http://dl.acm.org/citation.cfm?id=1775967.1775976},
acmid = {1775976},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
}