Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Hu, Xuemei; Huang, Lan; Wang, Yan; Pang, Wei

doi:10.1016/j.asoc.2019.105500

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…The pace of movement is defined as Equation (2).

where

is the difference between the location of

and its center,

is a weight for

, and its default value is 0.068 in [ 50 ]. In this step, the location for

is updated as Equation (4).…”

Section: Methodsmentioning

confidence: 99%

“…In this step, the location for

is updated as Equation (4).

where

are the weights, and the default value of

is equal to

; the default value of

is 0.01 in [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

“…dust location dust lcation w dis dust flag w rand (4) where  12 0, 0 ww are the weights, and the default value of = 1 0.068 w is equal to w ; the default value of 2 w is 0.01 in [50].…”

mentioning

confidence: 99%

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EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Huang

Wang

et al. 2022

Entropy

Self Cite

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Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

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“…The pace of movement is defined as Equation (2).

where

is the difference between the location of

and its center,

is a weight for

, and its default value is 0.068 in [ 50 ]. In this step, the location for

is updated as Equation (4).…”

Section: Methodsmentioning

confidence: 99%

“…In this step, the location for

is updated as Equation (4).

where

are the weights, and the default value of

is equal to

; the default value of

is 0.01 in [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Huang

Wang

et al. 2022

Entropy

Self Cite

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Creation of a Unique Clustering Method Employing Novel Similarity Metrics for Legal Texts to Improve Information Management and Retrieval in the Legal Field

Jain,

Goel

2024

Data-Intensive Research

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EGFA-NAS: a neural architecture search method based on explosion gravitation field algorithm

Hu,

Huang,

Zeng

et al. 2023

Complex Intell. Syst.

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Neural architecture search (NAS) is an extremely complex optimization task. Recently, population-based optimization algorithms, such as evolutionary algorithm, have been adopted as search strategies for designing neural networks automatically. Various population-based NAS methods are promising in searching for high-performance neural architectures. The explosion gravitation field algorithm (EGFA) inspired by the formation process of planets is a novel population-based optimization algorithm with excellent global optimization capability and remarkable efficiency, compared with the classical population-based algorithms, such as GA and PSO. Thus, this paper attempts to develop a more efficient NAS method, called EGFA-NAS, by utilizing the work mechanisms of EGFA, which relaxes the search discrete space to a continuous one and then utilizes EGFA and gradient descent to optimize the weights of the candidate architectures in conjunction. To reduce the computational cost, a training strategy by utilizing the population mechanism of EGFA-NAS is proposed. In addition, a weight inheritance strategy for the new generated dust individuals is proposed during the explosion operation to improve performance and efficiency. The performance of EGFA-NAS is investigated in two typical micro search spaces: NAS-Bench-201 and DARTS, and compared with various kinds of state-of-the-art NAS competitors. The experimental results demonstrate that EGFA-NAS is able to match or outperform the state-of-the-art NAS methods on image classification tasks with remarkable efficiency improvement.

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Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Cited by 3 publications

References 38 publications

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Creation of a Unique Clustering Method Employing Novel Similarity Metrics for Legal Texts to Improve Information Management and Retrieval in the Legal Field

EGFA-NAS: a neural architecture search method based on explosion gravitation field algorithm

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