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8 articles for “Multi-population genetic algorithm”
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Optimization of Production Scheduling using Multi-Tasking by Multi-Population Genetic Algorithm
Abstract: Production scheduling is an important activity for manufacturing and engineering, where it can have a major impact on the productivity of a production system. Traditional in-house manufacturing involves production machines with dedicated operators. Here, multitasking technique is used to optimize the production scheduling problem. In parallel machine scheduling, there are n jobs and m machines and each job needs to be executed on one of the machines during a fixed …
Published in Journal of Production Research & Management · Vol. 5, Issue 1, 2015 · pp. 18–26 Read article
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Optimal Size and Location of Capacitor Bank for Reactive Power Compensation Using Genetic Algorithm
Abstract: Power system operators are always faced with the dilemma of how to reduce the transmission loss. There are many ways to attain this target. In this thesis, new method for optimal capacitor placement for transmission loss minimization is planned. Proposed methods are based on the optimal capacitor placement formulations, which allow the cost benefit analysis and multi-objective optimization consideration of reactive power support investment. For better design of proposed methods, …
Published in Journal of VLSI Design Tools and Technology · Vol. 9, Issue 2, 2019 · pp. 28–34 Read article
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Integrating Genetic Algorithms with Lean Manufacturing for Enhanced Production Efficiency
Abstract: Lean manufacturing is a well-established philosophy focusing on the systematic reduction of waste and the ongoing development of value supplied to the customer. It emphasizes efficiency, quality, and adaptability through ideas such as just-in-time production, continuous improvement (Kaizen), and value stream optimization. However, the increased complexity of modern production systems, driven by global rivalry, product variety, and rapid technology innovation, has shown the limitations of classic lean tools in achieving …
Published in Journal of Production Research & Management · Vol. 15, Issue 3, 2025 · pp. 38–43 Read article
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Binary GA for Sidelobe Reduction and Null Steering in 5G Array
Abstract: This paper presents a comprehensive performance evaluation of a Binary Genetic Algorithm (BGA) for optimizing radiation characteristics of an active phased array antenna used in fifth-generation (5G) wireless communication systems. The primary objective is to achieve effective null steering while maintaining the desired main beam direction and simultaneously reducing the side lobe level (SLL). These improvements are essential for minimizing co-channel interference, enhancing spatial selectivity, and improving the Signal-to-Interference-plus-Noise Ratio …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 2, 2026 Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article
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AI-Driven Precision Nutrition: Advancing Personalized Dietary Systems for Public Health Equity in Resource-Constrained Environments
Abstract: The dual burden of malnutrition and diet-related non-communicable diseases (NCDs) represents a growing global public health challenge, particularly in low- and middle-income countries. Traditional dietary guidelines are largely population-based and fail to account for individual variability in genetics, metabolism, lifestyle, and environmental exposure. This limitation has led to the emergence of precision nutrition, an evolving field that integrates biological data and computational intelligence to deliver personalized dietary recommendations. This paper …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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Numerical Simulation of Hybrid GSA Based Optimal Power Flow for Multi Objective Optimization Strategy
Abstract: Restricted nonlinear optimization in electric power systems engineering is a topic of Optimal Power Flow (OPF) that has been extensively investigated. It has been a long and remarkable history for the OPF, which was founded in the 1960s, of research and publication. Newcomers to OPF research face a challenging undertaking since there is so much information available and because OPF's popularity within the electric power systems community has prompted authors …
Published in Journal of Instrumentation Technology & Innovations · Vol. 11, Issue 3, 2021 · pp. 24–32 Read article