Discovering promising regions to help global numerical optimization algorithms
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Data
2007-01-01
Autores
Melo, Vinicius V. de
Delbem, Alexandre C. B.
Pinto Junior, Dorival L.
Federson, Fernando M.
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Springer
Resumo
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.
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Micai 2007: Advances In Artificial Intelligence. Berlin: Springer-verlag Berlin, v. 4827, p. 72-82, 2007.