A Novel Selection Approach for Genetic Algorithms for Global Optimization of Multimodal Continuous Functions

Haq, Ehtasham-ul; Ahmad, Ishfaq; Hussain, Abid; Almanjahie, Ibrahim M.

doi:10.1155/2019/8640218

Cited by 22 publications

(12 citation statements)

References 29 publications

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“…Benchmark functions are a useful tool to verify the effectiveness of a method, and it is general to use several functions with different properties, such as in [ 29 , 30 ]. We selected 15 benchmark functions with different characteristics from the literature [ 31 – 33 ] for evaluation.…”

Section: Resultsmentioning

confidence: 99%

A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm

Nakane

Zhang

2020

Computational Intelligence and Neuroscience

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In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.

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Section: Resultsmentioning

confidence: 99%

A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm

Nakane

Zhang

2020

Computational Intelligence and Neuroscience

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“…Because ClGeA correlated global stability area and local incentive area on the basis of the seed cluster, the cluster generated by ClGeA took into account both global stability and local local exploitation [32][33][34]. Besides, it also had good diversity [35][36][37] among clusters. Take 6   = as an example in Figure 2.…”

Section: Quarmentioning

confidence: 99%

Principal Component Regression Analysis for lncRNA-Disease Association Prediction Based on Pathological Stage Data

Wang

Zhang

2021

IEEE Access

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Accumulating researches have found that lncRNAs play a key role in many important biological processes, such as chromatin modification, transcription, and post-transcription regulation. Because lncRNAs play an important role in the life process, many important complex diseases have been linked to the variation and dysfunction of lncRNAs. In current prediction researches on lncRNA-disease association, clinical prognosis information of the disease (such as pathological stage, clinical stage and so on) is rarely mentioned. In this manuscript, we apply the pathological stage data into the lncRNA-disease association prediction. Firstly, coordinates reverse rotation in circular (CRRC) is proposed. 6 clusters are calculated by the proposed cluster generating algorithm (ClGeA) based on CRRC. Secondly, harmonic importance ranking (HIR) is put forward. 28 core variables are obtained by the proposed selection algorithm of core variables for cancer pathological stage (SA-CV-CPS) based on HIR and cluster. Finally, on the basis of the above 28 core variables, pathological stage prediction algorithm for lncRNA-disease association based on principal component regression analysis (PSPA-LA-PCRA) is developed. Through PSPA-LA-PCRA, principal component set (including 20 PCs) and prediction model are gained. The proposed prediction model is based on unknown human lncRNA-disease association combining with the pathological stage data. Experimental results show that better results for AUC, precision rate, recall rate and F1-score of the prediction model are achieved by PSPA-LA-PCRA, which provides a favorable research premise for subsequent prediction studies of lncRNA-disease associations.

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“…The training set was utilized in the development of the QSAR model, where the activities (dependent variable) and the molecular descriptors (independent variables) were exposed to model development by the Genetic Function Approximation (GFA) method contained in the material studio software. The Genetic algorithms (GAs), a copycat of biological evolution, are currently the ultimately used variable selection method that addresses both the constrained and unconstrained optimization challenges based on an essential selection process [19,20]. GA is a prying search method that looks for the correct or approximate solutions to any optimization challenges [21].…”

Section: Qsar Model Developmentmentioning

confidence: 99%

In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

et al. 2020

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A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

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A Novel Selection Approach for Genetic Algorithms for Global Optimization of Multimodal Continuous Functions

Cited by 22 publications

References 29 publications

A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm

A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm

Principal Component Regression Analysis for lncRNA-Disease Association Prediction Based on Pathological Stage Data

In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

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