Discovering promising regions to help global numerical optimization algorithms

Authors:
Vinícius V. De Melo;Alexandre C. B. Delbem;Dorival L. Pinto Júnior;Fernando M. Federson
Affiliations:
State University of São Paulo, São Carlos, SP, Brazil;State University of São Paulo, São Carlos, SP, Brazil;State University of São Paulo, São Carlos, SP, Brazil;State University of São Paulo, São Carlos, SP, Brazil
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

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.