Lens imaging opposition-based learning. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

<i>De Novo</i> Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization by Alberga Domenico (9356272)

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Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Summary of classification algorithms. by Stephen R. Piccolo (8367780)

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Classification algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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Summary of feature-selection algorithms. by Stephen R. Piccolo (8367780)

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Library of the 55 different classification and regression machine-learning algorithms used by the ensemble predictor SuperLearner (SL.library) in the CBDA 2.0 implementation. by Simeone Marino (81051)

Feature-selection algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

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Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

Tradeoff between execution time and predictive performance for classification algorithms. by Stephen R. Piccolo (8367780)

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RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based <i>de novo</i> drug discovery. …”

Relative classification performance per combination of feature-selection and classification algorithm. by Stephen R. Piccolo (8367780)

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Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

Flowchart of low-light image enhancement based on MESBOA. by Yuqi Xiong (12343771)

Comparison based on hard instances from [79]. by Xiang Tian (4369285)

“…Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. …”

Train test validation libraries. by Muhammad Usman Tariq (11022141)

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-value on 23 benchmark functions (dim = 30). by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

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Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. by Stephen R. Piccolo (8367780)

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