Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
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Otsu's between-class variance correlates more strongly with SSIM and PSNR than Kapur's entropy on BSDS500 images, revealing metric-objective bias in multilevel thresholding evaluation.
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Energy-Aware Metaheuristics
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
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Image Thresholding: Understanding Bias of Evaluation Metrics towards Specific Evaluation Functions
Otsu's between-class variance correlates more strongly with SSIM and PSNR than Kapur's entropy on BSDS500 images, revealing metric-objective bias in multilevel thresholding evaluation.