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Different from the existing hybrid operators that are commonly operated on chromosome level, the proposed operator is executed on gene level to combine the advantages of simulated binary crossover (SBX) with local search ability and differential evolution (DE) with strong global search capability.
Therefore, the proposed operator has been implemented on battery-operated platforms, one based on the GPU NVIDIA ION2 and another on the FPGA Spartan III, which perform at rates of 30 and 60 frames per second, respectively, when working with VGA resolution images (640 × 480).
The results show the usefulness and applicability of the proposed operator.
The proposed operator can be used with multiple distance functions and data types.
Moreover, a two-stage bound-checking mechanism and an adaptive parameter setting scheme are designed to assist the proposed operator.
Furthermore, five fewer transistors are needed to design the proposed operator in contrast with both operators SUM and CCW.
Similar(29)
Besides these newly proposed operators, mutation and crossover operators have been used.
Our proposed operators are compared with state-of-the-art genetic operators for permutation problems.
Additionally, we show how to use proposed operators for simultaneous classifier combination and ensemble pruning.
In addition, the results demonstrate that the proposed operators can be employed to design a confident robust optimisation process and are readily applicable to different meta-heuristics.
The proposed operators adapt to the requirements and objective preferences of each subproblem dynamically during the evolution, resulting in significant improvements on the overall performance of MOEA/D.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com