Relevant bibliographies by topics / Genetic Advance (GA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Genetic Advance (GA)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Genetic Advance (GA)"

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Fernando, Achela K., and A. W. Jayawardena. "Use of a supercomputer to advance parameter optimisation using genetic algorithms." Journal of Hydroinformatics 9, no. 4 (2007): 319–29. http://dx.doi.org/10.2166/hydro.2007.006.

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Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 computer are used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 39
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Huang, Chien-Feng, Chi-Jen Hsu, Chi-Chung Chen, Bao Rong Chang, and Chen-An Li. "An Intelligent Model for Pairs Trading Using Genetic Algorithms." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/939606.

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Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results sho
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Roy, P. C., M. G. Rasul, M. A. K. Mian, and M. A. H. Molla. "GENETIC VARIABILITY OF RICE GENOTYPES (Oryza sativa L.)." Bangladesh Journal of Plant Breeding and Genetics 24, no. 2 (2011): 31–35. http://dx.doi.org/10.3329/bjpbg.v24i2.17004.

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An attempt was made to study genotypic variance, phenotypic variance, environmental variance, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), heritability (h2b) and genetic advance (GA) for some rice genotypes during July 2007 to January 2009 at BSMRAU campus, Gazipur 1706. Significant variations were obtained among the genotypes for all the characters studied. Considering genetic parameters high genotypic coefficient of variation (GCV) value was observed for harvest index followed by yield per hill, number of tillers per hill, number of filled grain per pa
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Elbaz, Khalid, Shui-Long Shen, Annan Zhou, Da-Jun Yuan, and Ye-Shuang Xu. "Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm." Applied Sciences 9, no. 4 (2019): 780. http://dx.doi.org/10.3390/app9040780.

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The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prio
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S. P., Ubale, Rasal P. N., Pundkar A.Y., Chaudhari S. R., Shelke P. R., and Gund M. S. "Assessment of Genetic Variability, Heritability and Genetic Advance in Wheat (Triticum aestivum L.) Genotypes under Different Sowing Dates." Journal of Advances in Biology & Biotechnology 28, no. 3 (2025): 107–19. https://doi.org/10.9734/jabb/2025/v28i32074.

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Twenty diverse wheat genotypes were evaluated for genetic variability, heritability and genetic advance under under three temperature conditions at the Research Farm of Department of Genetics and Plant Breeding, Mahatma Phule Krishi Vidyapeeth, Rahuri, Maharashtra, India. The genotypes were grown in randomized block design and data were collected for various morpho-agronomic characters. Analysis of observed data showed that the mean squares due to treatments for all the traits in all environments were highly significant. GCV and PCV were the highest for canopy temperature depression, grain yie
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Ibarra, José Ramón Meza, Joaquın Martınez Ulloa, Luis Alfonso Moreno Pacheco, and Hugo Rodrıguez Cortes. "Altitude Controller Based on Artificial Neural Network Genetic Algorithm for a Quadcopter MAV." International Journal of Robotics and Control Systems 4, no. 4 (2024): 1862–85. https://doi.org/10.31763/ijrcs.v4i4.1582.

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Mechanical systems with high dynamic complexity often face challenges due to unmodeled uncertainties and external perturbations, making effective control difficult. Therefore, new advanced, robust, intelligent control theories have been developed through the sudden advance of computational power in recent years. In this research work, these new theories of automatic control are used, mainly based on what is currently called Artificial Intelligence (AI) algorithms, to develop a novel altitude controller based on the theory of Genetic Algorithms (GA) and Artificial Neural Networks (ANN).Theperfo
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Umar, Muhammad, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, K. S. Al-Basyouni, S. R. Mahmoud, and Yolanda Guerrero Sánchez. "An Advance Computing Numerical Heuristic of Nonlinear SIR Dengue Fever System Using the Morlet Wavelet Kernel." Journal of Healthcare Engineering 2022 (January 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/9981355.

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This study is associated to solve the nonlinear SIR dengue fever system using a computational methodology by operating the neural networks based on the designed Morlet wavelet (MWNNs), global scheme as genetic algorithm (GA), and rapid local search scheme as interior-point algorithm (IPA), i.e., GA-IPA. The optimization of fitness function based on MWNNs is performed for solving the nonlinear SIR dengue fever system. This MWNNs-based fitness function is accessible using the differential system and initial conditions of the nonlinear SIR dengue fever system. The designed procedures based on the
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Alquraish, Mohammed, Khaled Ali. Abuhasel, Abdulrahman S. Alqahtani, and Mosaad Khadr. "SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting." Sustainability 13, no. 22 (2021): 12576. http://dx.doi.org/10.3390/su132212576.

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Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI)
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Lin, Yang-Kuei, and Chen-Hao Yen. "Genetic Algorithm for Solving the No-Wait Three-Stage Surgery Scheduling Problem." Healthcare 11, no. 5 (2023): 739. http://dx.doi.org/10.3390/healthcare11050739.

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In this research, we consider a deterministic three-stage operating room surgery scheduling problem. The three successive stages are pre-surgery, surgery, and post-surgery. The no-wait constraint is considered among the three stages. Surgeries are known in advance (elective). Multiple resources are considered throughout the surgical process: PHU (preoperative holding unit) beds in the first stage, ORs (operating rooms) in the second stage, and PACU (post-anesthesia care unit) beds in the third stage. The objective is to minimize the makespan. The makespan is defined as the maximum end time of
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Chauhan, Sugandh, Anuj Gupta, Sunil Dutt Tyagi, and Satpal Singh. "Genetic Variability, Heritability and Genetic Advance Analysis in Bread Wheat (Triticum aestivum L.) Genotypes." International Journal of Plant & Soil Science 35, no. 19 (2023): 164–72. http://dx.doi.org/10.9734/ijpss/2023/v35i193538.

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Wheat (Triticum aestivum L.) is one the most important cereal crop grown worldwide. The genetic improvement of any breeding population largely depends on the extent of genetic variability present in a crop species. In the present investigation, forty diverse bread wheat genotypes were evaluated for genetic variability, heritability, and genetic advance at the Research Farm of Kisan (PG) College, Simbhaoli, Hapur (U.P.) during rabi season 2021-22. The genotypes were grown in randomized block design with three replications and data were collected on eleven morphological characters. Analysis of v
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Dissertations / Theses on the topic "Genetic Advance (GA)"

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Cavalcanti, Bruno Jácome. "Análise de modelos de predição de perdas de propagação em redes de comunicações LTE e LTE-Advanced usando técnicas de inteligência artificial." PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/25061.

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Book chapters on the topic "Genetic Advance (GA)"

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Takeda, Fumiaki, and Sigeru Omatu. "A neuro-money recognition using optimized masks by GA." In Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60607-6_13.

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Chunka, Chukhu, Rajat Subhra Goswami, and Subhasish Banerjee. "A Novel Approach to Generate Symmetric Key in Cryptography Using Genetic Algorithm (GA)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1951-8_64.

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Anjum, Chaudhary Muhammad Shahbaz, and Aftab Khan. "Embedding Predecessor Information in Optimization of Genetic Algorithm (GA) Based Blind Image Restoration." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97051-1_14.

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Kim, Eunsu, Manseok Kim, and Jong-Wook Kim. "Optimal Trajectory Generation for Walking Up and Down a Staircase with a Biped Robot Using Genetic Algorithm (GA)." In Advances in Robotics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03983-6_15.

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Chen Shin-Shou, Huang Chien-Feng, Hong Tzung-Pei, and Chang Bao-Rong. "Using a Genetic Model for Asset Allocation in Stock Investment." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-484-8-167.

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In this paper, we present a study of asset allocation using genetic algorithms. This method extends a previous version of a stock selection model using Genetic Algorithms (GA) for solving the problem of asset allocation. The GA is used for optimization of model parameters, feature selection as well as the construction of the Pareto front. On top of that, we proposed another GA to search for the optimal allocation of assets. We then present an investigation for this line of research using financial data of various industrial sectors in Taiwan's stock market. Our experimental results show that o
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"Advance GA Operators and Techniques in Search and Optimization." In Advances in Computational Intelligence and Robotics. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4105-0.ch008.

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Genetic algorithms (GAs) are the latest technique to solve problems. A huge amount of research work is available but still there are a lot of newer avenues which have to be explored. In this chapter, the authors discuss variations in the GA operators that can be done and various other background operators that can be use to improve the efficiency and efficacy of GAs. It is not just like a mathematical or statistical technique. In this chapter, the authors discuss various procedural variations, twists and turns, and special meaning they could give to the GA operators in order to improve the sol
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K. Mohammed, Ibrahim. "Design of Optimized PID Controller Based on ABC Algorithm for Buck Converters with Uncertainties." In Advance Innovation and Expansion of PID Controllers [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94907.

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Proportional Integral Derivative (PID) is the most popular controller that is commonly used in wide industrial applications due to its simplicity to realize and performance characteristics. This technique can be successfully applied to control the behavior of single-input single-output (SISO) systems. Extending the using of PID controller for complex dynamical systems has attracted the attention of control engineers. In the last decade, hybrid control strategies are developed by researchers using conventional PID controllers with other controller techniques such as Linear Quadratic Regulator (
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Simon, Herbert A. "Good Old-Fashioned AI and Genetic Algorithms: An Exercise in Translation Scholarship." In Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162929.003.0013.

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In both the GA and GOFAI traditions, invention or design tasks are viewed as instances of problem solving. To invent or design is to describe an object that performs, in a range of environments, some desired function or serves some intended purpose; the process of arriving at the description is a problem-solving process. In problem solving, the desired object is characterized in two different ways. The problem statement or goal statement characterizes it as an object that satisfies certain criteria of structure and/or performance. The problem solution describes in concrete terms an object that
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Gondra, Iker. "Parallelizing Genetic Algorithms." In Artificial Intelligence for Advanced Problem Solving Techniques. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-705-8.ch012.

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Genetic Algorithms (GA), which are based on the idea of optimizing by simulating the natural processes of evolution, have proven successful in solving complex problems that are not easily solved through conventional methods. This chapter introduces their major steps, operators, theoretical foundations, and problems. A parallel GA is an extension of the classical GA that takes advantage of a GA’s inherent parallelism to improve its time performance and reduce the likelihood of premature convergence. An overview of different models for parallelizing GAs is presented along with a discussion of th
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Mishra, Vinod Kumar. "Application of Genetic Algorithms in Inventory Control." In Advances in Logistics, Operations, and Management Science. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9888-8.ch003.

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The genetic algorithm (GA) is an adaptive heuristic search procedures based on the mechanics of natural selection and natural genetics. Inventory control is widely used in the area of mathematical sciences, management sciences; system science, industrial engineering, production engineering etc. but they have wide differences in mathematical and computation maturity. This chapter enables the reader to understand the basic theory of genetic algorithm and how to apply the genetic algorithms for optimizing the parameters in inventory control The current and future trend of the research with the de
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Conference papers on the topic "Genetic Advance (GA)"

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Rekha, V., S. Kavitha, T. Santhi Punitha, S. Aarthy, Baydaa Sh Z. Abood, and Huthaifa Alani. "The Way of Creating GA (Genetic Algorithm) for Controlled and Secured Data Management in Cloud Environment." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10616726.

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Setiyanti, Michelle, Genrawan Hoendarto, and Jimmy Tjen. "Enhancing Water Potability Identification through Random Forest Regression and Genetic Algorithm Optimization." In INTERNATIONAL CONFERENCE ON APPLIED TECHNOLOGY 2024. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-2fikqf.

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Water quality is important for both environmental sustainability and public health. This research introduces an innovative method for forecasting water quality using Random Forest Regression, optimized through Genetic Algorithm (GA) techniques. The goal is to enhance prediction accuracy and offer meaningful insights for better water resource management. The study employed the “Water Quality Data” dataset, encompassing 11 essential water quality parameters from different locations. After thorough data preprocessing, the Random Forest model, refined with GA optimization, achieved a Mean Squared
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Karim, Mashud, and Mitsuhisa Ikehata. "A Genetic Algorithm (GA) Based Optimization Technique for the Design of Marine Propeller." In SNAME 9th Propeller and Shafting Symposium. SNAME, 2000. http://dx.doi.org/10.5957/pss-2000-16.

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A genetic algorithm (GA) based optimization technique for the design of systematic series propellers, e.g., B-series, Gawn, Newton-Rader series etc. is studied in this research and its applicability has been verified. Using polynomial expressions for thrust and torque coefficients of these propellers as functions of blade area ratio, pitch ratio, advance coefficient and number of blades, GA searches for an improved propeller of maximum efficiency under some design requirements and imposed constraints. In this study, GA is found as a reliable tool for optimization of marine propeller.
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Ruppin, Eytan. "Abstract SY45-02: Harnessing genetic interactions to advance whole-exome precision cancer treatment." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-sy45-02.

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Ruppin, Eytan. "Abstract SY45-02: Harnessing genetic interactions to advance whole-exome precision cancer treatment." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-sy45-02.

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Ivačko, Nikola, Ivan Ćirić, Ljiljana Radović, and Žarko Ćojbašić. "Implementation of Genetic Algorithms in Convolutional Neural Networks for Object Detection and Classification." In XVII International Conference on Systems, Automatic Control and Measurements. University of Niš, Faculty of Electronic Engineering, Faculty of Mechanical Engineering, Niš, 2024. https://doi.org/10.46793/saum24.161i.

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Convolutional Neural Networks (CNNs) have established themselves as a cornerstone in object detection and classification, delivering exceptional performance across many applications. However, the efficacy of CNNs is heavily dependent on the meticulous design and optimization of their architecture and hyperparameters, a process that is often labor-intensive and computationally demanding. Genetic Algorithms (GAs), inspired by the principles of natural evolution, present a viable solution to automate and enhance the optimization of CNNs. This survey reviews the integration of genetic algorithms i
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Kawagishi, Hiroyuki, and Kazuhiko Kudo. "Development of Global Optimization Method for Design of Turbine Stages." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68290.

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A new optimization method which can search for the global optimum solution and decrease the number of iterations was developed. The performance of the new method was found to be effective in finding the optimum solution for single- and multi-peaked functions for which the global optimum solution was known in advance. According to the application of the method to the optimum design of turbine stages, it was shown that the method can search the global optimum solution at approximately one seventh of the iterations of GA (Genetic Algorithm) or SA (Simulated Annealing).
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Lee, Jin-Woo, Kuk Jin Jung, Morely Sherman, Hyun Sin Kim, and Youn-Jea Kim. "Experimental and Numerical Analysis on the Performance of Spiral Two-Fluid Atomizer Using DPM Method." In ASME 2020 Fluids Engineering Division Summer Meeting collocated with the ASME 2020 Heat Transfer Summer Conference and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/fedsm2020-20350.

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Abstract A two-fluid atomizer has been frequently used in a wide range of industries for various purposes such as painting, cleaning particles and snow making. In particular, the manufacturing of advance semiconductors using sensitive devices such as organic light emitting diodes (OLED) and dynamic random access memory (DRAM), require high performance nozzle. The droplets sprayed with a high relative gas velocity are widely used for cleaning particles. In this paper, two-fluid atomizer is numerically studied according to four variables to confirm the effect on the atomizer performance. The num
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Sampath, Suresh, Stephen Ogaji, and Riti Singh. "Improving Power Plant Availability Through Advanced Engine Diagnostic Techniques." In 2002 International Joint Power Generation Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ijpgc2002-26080.

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Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas turbine and combined cycle models able to undertake appropriate diagnostics and life cycle costing. These are complex areas as they include the simulation of such issues as performance and assessment of degraded gas turbines, life usage and risk analysis. This paper describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Networks (ANN) and Genetic
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ZIMMERLING, C. "Forming process optimisation for variable geometries by machine learning – Convergence analysis and assessment." In Material Forming. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902479-126.

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Abstract. For optimum operation, modern production systems require a careful adjustment of the employed manufacturing processes. Physics-based process simulations can effectively support this process optimisation; however, their considerable computation times are often a significant barrier. One option to reduce the computational load is surrogate-based optimisation (SBO). Although SBO generally helps improve convergence, it can turn out unwieldy when the optimisation task varies, e.g. due to frequent component adaptations for customisation. In order to solve such variable optimisation tasks,
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