Algorithm timings on a laptop computer with Intel i9–9900K CPU, 32GB RAM and an NVIDIA RTX2080 GPU. by Adam L. Tyson (10892822)

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Sensitivity analysis of the TV-BayesOpt algorithm with a forgetting (orange line) or a forgetting-periodic (blue line) covariance function for a range of ε values. by John E. Fleming (8533956)

“…<p>Incorporation of prior knowledge of the temporal variation in the objective function optimum value (blue line) resulted in improved TV-BayesOpt algorithm performance than implementing a forgetting covariance function (orange line) alone. …”

Fixation time on slow oriented graphs. by David A. Brewster (17118328)

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Comparison of results of different algorithms. by Zhibo Fu (19009854)

“…The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. …”

Variations in cost effectiveness ratios of the eight Ebola screening algorithms as a function of the cost of standard-of-care and QuickNavi™-Ebola RDT. by Antoine Oloma Tshomba (14271425)

The results of each test function. by Yuhang Wang (332111)

Evolution of objective function. by Serdar Ekinci (15927455)

“…A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. …”

Objective function value statistics. by Chao Li (145513)

Structural diagram of the overall SCF-YOLO algorithm. by Yazhou Li (277980)

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Ave and Std of different algorithms. by Yanzhao Gu (21192659)

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Parameter settings for metaheuristic algorithm. by Yanzhao Gu (21192659)

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The convergence curves of all the 10 algorithms. by WanRu Zhao (18980374)

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Fixation time is not monotone in <i>r</i>. by David A. Brewster (17118328)

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Two-dimensional benchmark test-functions. by Salisu Mohammed (772274)

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Thirty-dimensional benchmark test-functions. by Salisu Mohammed (772274)

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PFC value of algorithms on two datasets. by Xing Chen (140292)

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Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes. by Wenjun Zhao (644283)

“…<p>Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes.…”

Comparison between MaAVOA and other algorithms in terms of the number of generation and number of function evaluations on DTLZs in case of the computational time is 30 seconds. by Heba Askr (15572851)

“…<p>Comparison between MaAVOA and other algorithms in terms of the number of generation and number of function evaluations on DTLZs in case of the computational time is 30 seconds.…”

Performance of the greedy approximation algorithm. by Virgile Andreani (17087764)

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”