Genetic Algorithm: Reviews, Implementations, and Applications

Alam, Tanweer; Qamar, Shamimul; Dixit, Amit Rai; Benaida, Mohamed

doi:10.48550/arxiv.2007.12673

Cited by 14 publications

(17 citation statements)

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“…In more recent years GAs have been used for a variety of applications outside of life sciences, such as scheduling and finding the shortest path, as well as in modeling and simulation where the use of random functions is required [196]. In comparison, evolutionary algorithms are utilized to tackle problems for which there is no well-established, efficient solution [197]. The overview of GA is shown in figure 9.…”

Section: Genetic Algorithms (Ga) In Microscopymentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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Electron microscopy, a sub-field of microanalysis, is a victim of its own success. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyse it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning, a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different deep learning approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of deep learning is designed to guide electron microscopists to choose the right deep learning algorithm for their research and prepare for their digital future.

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Section: Genetic Algorithms (Ga) In Microscopymentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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“…Machine learning, a subset of AI, is dominating medical diagnosis. For the detection and classification of abnormal cell images over the past ten years, a variety of machine learning techniques and CAD systems have been used, including K-nearest neighbours [37], [38], support vector machines (SVM) [39], [40], [41], artificial neural networks (ANN) [42], [43], random forests [44], [45], etc. Table 2 provides a comprehensive summary of recent works published in the domain of cervical cancer detection.…”

Section: Literature Surveymentioning

confidence: 99%

“…individuals over time [43]. Genetic algorithms are implemented as a simulation representing the decision variables of a search problem as finite-length strings of alphabets of specific cardinality called chromosomes [44]. Genes are represented as the alphabet.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Iqbal,

Qureshi,

Chishti

2024

Preprint

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Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. A lack of effective early diagnosis is the fundamental cause of the disease's prevalence and poses the greatest obstacle to researchers. Pap smears, human papillomavirus tests, colposcopy, and biopsies are all invasive procedures that necessitate the assistance of medical professionals; they are time-consuming and do not identify cancer until a later stage. Thus, the absence of early symptoms and lack of invasive diagnostics, highlight the need for utilizing machine learning and deep learning approaches to revolutionize cervical cancer detection. To address this, we propose GCFS-CC(Genetic Algorithm based Context-aware Feature Selection for Cervical Cancer), which is a framework to detect cervical cancer from Pap smear images of cervical cells and classify these using a deep learning method. In our work, we use a genetic algorithm which is a bio-inspired algorithm, to select the best features from the acquired images. For feature selection, we incorporate the context-aware entropy gain method. We evaluated our model on one data set which combined a total of 917 normal and cancerous images, divided into seven classes. Using the selected features we build a deep learning model to validate our proposed framework. The results showed that after feature selection, the performance metrics(accuracy, precision, and recall), all improved. These results pave the way for GCFS-CC to be a valuable tool for early cervical cancer detection. This, however, comes with certain challenges, availability of high-quality images and proper parameter tuning for the genetic algorithm required for feature selection. To successfully implement machine learning-based cervical cancer detection into clinical practice, it will be necessary to address these challenges. Furthermore, any ethical and privacy concerns associated with the use of machine learning algorithms in healthcare settings must be addressed.

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“…Multi-objective optimization aims to find a set of solutions that simultaneously optimize multiple objectives, considering trade-offs between them [14]. GAs are inspired by the process of natural selection and evolution [15]. They operate on a population of potential solutions, represented by chromosomes, and iteratively improve them through selection, crossover, and mutation operations.…”

Section: Multi-objective Message Routing In Enfvsmentioning

confidence: 99%

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

Alolaiwy,

Hawsawi,

Zohdy

et al. 2023

Applied Sciences

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The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.

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Genetic Algorithm: Reviews, Implementations, and Applications

Cited by 14 publications

References 0 publications

Advancing electron microscopy using deep learning

Advancing electron microscopy using deep learning

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

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