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- W4385189768 abstract "This paper proposes a technique combining machine learning and an evolutionary algorithm for solving a graph-based multiobjective optimization problem related to epidemics control. In the studied problem, graph nodes can be protected, by vaccinations, against a simulated epidemic. To solve this problem, a classifier-based multiobjective evolutionary algorithm is proposed, in which population initialization, crossover, mutation and local search are guided by a previously trained machine learning model (a classifier). In the experiments, several machine learning models were considered: the multilayer perceptron (MLP) neural network, the Naïve Bayes classifier, the Support Vector Machine (SVM) and several simpler classifiers. The multilayer perceptron (MLP) neural network was found to be the best performing classifier. The proposed method outperformed an evolutionary optimizer not using any classifier-based components. Results of the experiments shown that knowledge can be transferred from smaller problem instances to larger ones using trained machine learning models. The MLP classifier trained on data collected from instances with 1000 graph nodes maintained good performance when used on instances with up to 20000 nodes." @default.
- W4385189768 created "2023-07-25" @default.
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- W4385189768 date "2023-07-15" @default.
- W4385189768 modified "2023-09-27" @default.
- W4385189768 title "Classifier-based evolutionary multiobjective optimization for the graph protection problem: Hot-off-the-Press Track at GECCO 2023" @default.
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- W4385189768 doi "https://doi.org/10.1145/3583133.3595843" @default.
- W4385189768 hasPublicationYear "2023" @default.
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