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Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve

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Using Smart Sampling to Discover Promising Regions and Increase the Efficiency of Differential Evolution

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

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On Promising Regions and Optimization Effectiveness of Continuous and Deceptive Functions

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

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Improving Global Numerical Optimization using a Search-space Reduction Algorithm

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

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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

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