On the origin of the evolutionary computation species influences of Darwin’s theories on computer science

Serrano, J. Ignacio; Castillo, M. Dolores del

doi:10.1007/s10462-011-9246-6

Cited by 3 publications

(1 citation statement)

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“…The concept of evolutionary computation is based on Darwin's theory of biological evolution [26]. EA are used to search for optimal solutions to optimization problems that are considered NP-hard problems.…”

Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

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The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workaround for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, providing a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups with discussion of the nature of the representation schemes for each category identified in the survey. This review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions are presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities of metaheuristic-based algorithms in the context of PPARM.

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Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

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Optimizing a Convolutional Neural Network with a Hierarchical Genetic Algorithm for Diabetic Retinopathy Detection

Cordero-Martínez

Sánchez

Melín

2023

Studies in Computational Intelligence

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Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification

Cordero-Martínez¹,

Sánchez²,

Melín³

2022

HIS

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Diabetic retinopathy (DR) is one of the worse conditions caused by diabetes mellitus (DM). DR can leave the patient completely blind because it may have no symptoms in its initial stages. Expert physicians have been developing technologies for early detection and classification of DR to prevent the increasing number of patients. Some authors have used convolutional neural networks for this purpose. Pre-processing methods for database are important to increase the accuracy detection of CNN, and the use for an optimization algorithm can further increase that accuracy. In this work, four pre-processing methods are presented to compare them and select the best one. Then the use of a hierarchical genetic algorithm (HGA) with the pre-processing method is done with the intention of increasing the classification accuracy of a new CNN model. Using the HGA increases the accuracies obtained by the pre-processing methods and outperforms the results obtained by other authors. In the binary study case (detection of DR) a 0.9781 in the highest accuracy was achieved, a 0.9650 in mean accuracy and 0.007665 in standard deviation. In the multi-class study case (classification of DR) a 0.7762 in the highest accuracy, 0.7596 in mean accuracy and 0.009948 in standard deviation.

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On the origin of the evolutionary computation species influences of Darwin’s theories on computer science

Cited by 3 publications

References 50 publications

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Optimizing a Convolutional Neural Network with a Hierarchical Genetic Algorithm for Diabetic Retinopathy Detection

Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification

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