A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification

Le, Hoang Lam; Landa-Silva, Dario; Galar, Mikel; García, Salvador; Triguero, Isaac

doi:10.1109/cec48606.2020.9185774

Cited by 5 publications

(1 citation statement)

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“…As another GA with machine learning, the surrogate-assisted GA is proposed [8][9][10]. This GA uses the surrogate model to approximate an objective function, and the surrogate model is trained during the execution of the GA.…”

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confidence: 99%

Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems

Iima

Hazama

2022

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics &Amp; Swarm Intelligence

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This paper addresses an optimization problem with two decision variable vectors. This problem can be divided into multiple subproblems when an arbitrary value is given to the first decision variable vector. In conventional genetic algorithms (GAs) for the problem, an individual is often expressed by the value of the first decision variable vector. In evaluating the individual, the value of the remaining decision variable vector is determined by metaheuristics or greedy algorithms. However, such GAs are time-consuming or not general-purpose. We propose a GA with a neural network model to estimate the optimal objective function values of the subproblems. Experimental results compared to other GAs show that the proposed method is effective.

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confidence: 99%