Overall rankings of optimization algorithms. by Máté Mohácsi (20469514)

“…<p>Statistics of the ranks achieved by individual optimization algorithms on the different benchmarks involving multiple error components (Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g001" target="_blank">1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g004" target="_blank">4</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g005" target="_blank">5</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g006" target="_blank">6</a>) according to the final error (A) and convergence speed (B). …”

Model results for daily COVID-19 caused deaths after every step of the iterative optimization algorithm. by Martin Bicher (10852788)

Overall rankings of single-objective optimization algorithms. by Máté Mohácsi (20469514)

“…<p>Statistics of the ranks achieved by single-objective optimization algorithms on the six different benchmarks (Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g001" target="_blank">1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g006" target="_blank">6</a>) according to the final error (A) and convergence speed (B). …”

Model results for daily COVID-19 caused deaths for simulations performed during the second step of the optimization algorithm. by Martin Bicher (10852788)

Accumulated model results for daily COVID-19 caused deaths for all the simulations performed during the iterative optimization. by Martin Bicher (10852788)

MEA-BP neural network algorithm flowchart. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

Fig 2 - by Adam Lampert (413952)

Attribute values for target and source cases. by Feng Li (30515)

Attribute values for target and source cases. by Feng Li (30515)

Computation results of similar cases. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

The results of fitting input fluxes and initial concentrations of key molecular species in a subcellular biochemical network model. by Máté Mohácsi (20469514)

“…(C) Plot showing the evolution of the cumulative minimum error during the optimization. (D) Box plot representing the distribution of the final error scores over 10 independent runs of each algorithm. …”

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

Feature attribute weight cloud map. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

CBR problem-solving process. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

MEA subpopulation convergence processes. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Cloud model evaluation values for each indicator. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

On-site emergency response in mine B. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Parameter settings. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Weight values of basic characteristic attributes. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”