A review of surrogate-assisted evolutionary algorithms for expensive optimization problems

He, Chunlin; Zhang, Yong; Gong, Dunwei; Ji, Xinfang

doi:10.1016/j.eswa.2022.119495

Cited by 82 publications

(17 citation statements)

References 120 publications

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“…These predictions are used to select new points to evaluate the true objective function, aiming to improve the overall optimization process efficiently. The primary advantage of surrogate optimization is its ability to reduce the computational cost of optimization by replacing expensive function evaluations with inexpensive surrogate model predictions 35 .…”

Section: Proposed Methodsmentioning

confidence: 99%

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

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The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

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Section: Proposed Methodsmentioning

confidence: 99%

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

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“…They involve maintaining a population of candidate solutions and iteratively evolving them to optimize the objective function. 24,54,55 • Metaheuristic optimization: These are general-purpose optimization algorithms that can explore and exploit search spaces efficiently. Examples include simulated annealing, particle swarm optimization, ant colony optimization, and differential evolution.…”

Section: Additional Data-driven Optimization Strategiesmentioning

confidence: 99%

“…Examples include genetic algorithms, evolutionary strategies, and genetic programming. They involve maintaining a population of candidate solutions and iteratively evolving them to optimize the objective function 24,54,55 Metaheuristic optimization: These are general‐purpose optimization algorithms that can explore and exploit search spaces efficiently.…”

Section: Bo Of Dynamic Systemsmentioning

confidence: 99%

A data‐driven Bayesian approach for optimal dynamic product transitions

Flores‐Tlacuahuac,

Fuentes‐Cortés

2024

AIChE Journal

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In the processing industry, dynamic product transitions are essential for achieving high product quality, minimizing the use of raw materials and energy and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this article, a data‐driven Bayesian approach for optimal dynamic product transitions is proposed. The proposed approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first‐principles dynamic mathematical model for drawing optimal solutions. The approach is applied to three case studies, and the results are comparable in performance quality with those obtained using a traditional gradient‐based optimization approach. The results show that the proposed approach is able to find optimal transition trajectories that meet the product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real‐world applications. The proposed approach has several advantages over traditional optimization approaches, including being data driven, able to cope with noisy measurements, computationally efficient, and it requires modest computational effort. Complex online optimal control problems can benefit from adopting a data‐driven Bayesian optimization scheme.

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“…Surrogate-assisted evolutionary algorithms (SAEAs) [1][2][3] have been widely used to solve expensive optimization problems. In particular, SAEAs reduce the required solution evaluations by utilizing a surrogate model that estimates the objective function, and then only evaluating the promising solutions based on the estimated values.…”

Section: Introductionmentioning

confidence: 99%

Fast Implementation of Extreme Learning Machine-based DirectRanker for Surrogate-assisted Evolutionary Algorithms

Harada

2024

Preprint

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Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used to solve computationally expensive optimization problems. The extreme learning machine-based DirectRanker (ELDR) is a single-layer feed-forward neural network surrogate model designed for SAEAs. ELDR estimates the superiority of two solutions with a high estimation accuracy, even in high-dimensional problems. However, ELDR requires a long computation time as the problem dimensionality and the number of hidden neurons increase, thus making it difficult to apply it to high-dimensional problems. A surrogate model should be computationally efficient and enable rapid fitness estimations. Therefore, this paper proposes a fast implementation technique, i.e., fast version ELDR (fELDR) that achieves mathematically equivalent learning results with low computational complexity. Additionally, this paper proposes a pointwise score function to render the prediction results reusable. The experimental results confirmed the effectiveness of fELDR when compared with the original ELDR. The learning results of the proposed fELDR were equivalent to those of the original ELDR while reducing the training time by up to 97\%, especially when using a large hidden layer on a high dimensionality problem. Moreover, due to the reusable prediction results, the computation time of the fELDR-assisted SAEA can be further decreased by 79.5\% when compared with that of the original ELDR-assisted SAEA. The reduced training time and reusable prediction results of fELDR render it feasible to apply ELDR to high-dimensional optimization problems and realize a high prediction accuracy with a large number of hidden neurons.

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A review of surrogate-assisted evolutionary algorithms for expensive optimization problems

Cited by 82 publications

References 120 publications

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

A data‐driven Bayesian approach for optimal dynamic product transitions

Fast Implementation of Extreme Learning Machine-based DirectRanker for Surrogate-assisted Evolutionary Algorithms

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