This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by
This paper presents a novel method to discover promising regions in a continuous search space. Using machine learning techniques, the algorithm named smart sampling was tested in hard known benchmark functions, and was able to find promising regions with solutions very close to the global optimum, significantly decreasing the number of evaluations needed by a
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
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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