This paper evaluates the performance of three evolutionary algorithms to globally optimize complex continuous functions. The performance is evaluated by measuring the algorithms success rate to find the global optimum in several trials. At each set of trials, the search-space is reduced to be closer to the global optimum, so that the starting population is
We have developed an algorithm for reduction of search-space, called Domain Optimization Algorithm (DOA), applied to global optimization. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. DOA basically works using simple models for search-space regions to identify and eliminate non-promising regions. The proposed approach has shown relevant results
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
The current availability of information many times impair the tasks of searching, browsing and analyzing information pertinent to a topic of interest. This paper presents a methodology to create a meaningful graphical representation of documents corpora targeted at supporting exploration of correlated documents. The purpose of such an approach is to produce a map from
Vinícius Veloso de Melo is an Assistant Professor of Computer Science at
Federal University of São Paulo, São José dos Campos, SP, Brazil.
Email: (blanks against robots): myfirstname dot mylastname at unifesp dot br
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