Performance profile of the four algorithms based on the function value of iteration number. by Sulaiman M. Ibrahim (20614376)

“…<p>Performance profile of the four algorithms based on the function value of iteration number.…”

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

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Pseudo-code of DMDDPG algorithm. by Guohua Cao (697580)

“…Next, a reward function is designed by integrating the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm. …”

The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

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Curves of three activation functions. by Zhongzhong Guo (12133402)

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Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Compare the test functions. by Yanqiu Li (683621)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

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Empirical cumulative distribution function of RMSE for three sources. by Nauman Ahmed (8086868)

Genetic algorithm flowchart. by Junjie He (700642)

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Comparison of algorithm search curves. by Bowen Li (200859)

“…Then, combining with the idea of fast density clustering algorithm, the number of hidden layer neurons of RBF is determined by finding the point with the highest density and using it as the hidden layer neuron. …”

Pose errors of 5PUS-RPUR robot with different algorithms. by Zesheng Wang (15244172)

CEC2017 basic functions. by Tengfei Ma (597633)

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Heat maps of mean scores over 50 datasets of the algorithms as a function of relative noise and number of samples. by Yuanchen Zhao (12905580)

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One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

Output results based on three algorithms. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

GELU function. by Yuanzhou Zheng (15195249)

Iteration curves of different algorithms. by Tengfei Ma (597633)

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