Search alternatives:
policy optimization » topology optimization (Expand Search), wolf optimization (Expand Search), process optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary most » binary mask (Expand Search)
most based » costs based (Expand Search), test based (Expand Search), meat based (Expand Search)
binary b » binary _ (Expand Search)
b policy » tb policy (Expand Search), _ policy (Expand Search), a policy (Expand Search)
policy optimization » topology optimization (Expand Search), wolf optimization (Expand Search), process optimization (Expand Search)
based optimization » whale optimization (Expand Search)
binary most » binary mask (Expand Search)
most based » costs based (Expand Search), test based (Expand Search), meat based (Expand Search)
binary b » binary _ (Expand Search)
b policy » tb policy (Expand Search), _ policy (Expand Search), a policy (Expand Search)
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Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19.
Published 2021“…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
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Parameter settings of the comparison algorithms.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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Datasets and their properties.
Published 2023“…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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Parameter settings.
Published 2023“…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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