A gene expression programming approach for evolving multi-class image classifiers

Aquino, Nelson Marcelo Romero; Ribeiro, Matilde; Gutoski, Matheus; Benitez, Cesar Vargas; Lopes, Heitor Silvério

doi:10.1109/la-cci.2017.8285696

Cited by 1 publication

(1 citation statement)

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“…The contributions of our research extend beyond the specific context of wheat blossom midges, as they enhance the growing body of literature advocating the integration of genetic algorithms and machine learning in solving complex agricultural problems. Our work aligns with studies such as those by Al-Anni (2017) [33] and Aquino et al (2017) [34], which have documented the benefits of employing genetic programming and machine learning techniques in prediction. Employing genetic programming and machine learning in prediction offers several benefits, such as improved accuracy and efficiency in handling complex data.…”

Section: Discussionsupporting

confidence: 86%

Predictive Study on the Occurrence of Wheat Blossom Midges Based on Gene Expression Programming with Support Vector Machines

Li,

Lv,

Guo

et al. 2024

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This study addresses the challenges in plant pest and disease prediction within the context of smart agriculture, highlighting the need for efficient data processing techniques. In response to the limitations of existing models, which are characterized by slow training speeds and a low prediction accuracy, we introduce an innovative prediction method that integrates gene expression programming (GEP) with support vector machines (SVM). Our approach, the gene expression programming—support vector machine (GEP-SVM) model, begins with encoding and fitness function determination, progressing through cycles of selection, crossover, mutation, and the application of a convergence criterion. This method uniquely employs individual gene values as parameters for SVM, optimizing them through a grid search technique to refine genetic parameters. We tested this model using historical data on wheat blossom midges in Shaanxi Province, spanning from 1933 to 2010, and compared its performance against traditional methods, such as GEP, SVM, naive Bayes, K-nearest neighbor, and BP neural networks. Our findings reveal that the GEP-SVM model achieves a leading back-generation accuracy rate of 90.83%, demonstrating superior generalization and fitting capabilities. These results not only enhance the computational efficiency of pest and disease prediction in agriculture but also provide a scientific foundation for future predictive endeavors, contributing significantly to the optimization of agricultural production strategies.

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Section: Discussionsupporting

confidence: 86%