Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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Algorithm parameter levels. by Peng Yao (448155)

“…Alternatively, the formulated objective functions often deviate from real-world scenarios. …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

Our algorithms process large data sets within a minute. by Manu Aggarwal (15990691)

Iteration curves of each algorithm. by Peng Yao (448155)

“…Alternatively, the formulated objective functions often deviate from real-world scenarios. …”

Coarse-fine optimization algorithm. by Chao Ma (207385)

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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. …”

Running memory of the algorithms. by Yanju Zhang (437336)

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Comparison of algorithms across metrics in test problems. by Jihui Hu (19812736)

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Fitness function curve at weight factor 0.3. by Dhulfiqar Hakeem Dhayef (17729684)

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Selected optimal solutions from the QLNSGA-II algorithm. by Zhongfeng Li (3965060)

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Theoretical protection from mutations conferred by overlapping reading frames. by Antoine L. Decrulle (11540003)

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Flowchart for the Tabu Search Algorithm [43]. by Ferhat Yuna (21519851)

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Experimentally measured protection from mutations and expected resulting increase in circuit lifetime. by Antoine L. Decrulle (11540003)

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Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

ANOVA was performed to test the differences between the algorithms for each indicator. by Jihui Hu (19812736)

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Tuned parameters of algorithms. by Kaveh Faraji Googerdchi (18778727)