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MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the elitist genetic algorithm

ACS SYNTHETIC BIOLOGY 2025年第3期14卷 919-930页

作者： Zhang, Yangyu Chen, Guanlin Liang, Ce Yang, Bin Lei, Xin Chen, Tao Jiang, Huaiguang Xiong, Wei South China Univ Technol Sch Future Technol Guangzhou 511442 Guangdong Peoples R China Res Projects Dept Guangdong Artificial Intelligence & Digital Econ L Guangzhou 510220 Guangdong Peoples R China NIH Natl Inst Dent & Craniofacial Res Microbial Therapeut Unit Bethesda MD 20892 USA South China Univ Technol Sch Biol & Biol Engn Guangzhou 510006 Guangdong Peoples R China

Multiplexed CRISPR design, which allows for the concurrent and efficient editing of multiple genomic sites, is a powerful tool for complex genetic modifications. However, designing effective multiplexed guide RNA (gRNA) arrays remains challenging due to the exponential increase in potential gRNA array candidates and the significant impact of different target site selections on efficiency and specificity. Recognizing that more stable gRNAs, characterized by lower minimum free energy (MFE), have prolonged activity and thus higher efficacy, we developed MultiCRISPR-EGA, a graphical user interface (GUI)-based tool that employs the elitist genetic algorithm (EGA) to design optimized single-promoter-driven multiplexed gRNA arrays. Computational experiments on Escherichia coli gene targets demonstrate that the EGA can rapidly optimize multiplexed gRNA arrays, outperforming other intelligent optimization algorithms in CRISPR interference (CRISPRi) applications, while the GUI provides real-time design progress control and compatibility with various CRISPR-Cas systems. This tool aims to advance the multiplexed gRNA array design process, enabling more efficient and cost-effective genome editing for synthetic biology.

关键词： CRISPR interference elitist genetic algorithm genome editing guide RNA array design minimumfree energy multiplexed CRISPR

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elitist genetic algorithm Guided by Higher Order Statistic for Blind Separation of Digital Signals

Elitist Genetic Algorithm Guided by Higher Order Statistic f...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Gonzalez, E. A. Gorriz, J. M. Ramirez, J. Puntonet, C. G. Gonblan Consultores SLP Dept Consulting & ICT Dev Granada 18014 Spain Univ Granada Fac Ciencias Dept Signal Theory & Commun ES-18071 Granada Spain Univ Granada ETSI Informat Dept Architecture & Comp Technol ES-18071 Granada Spain

ISBN: (纸本)9781424452262

A novel method for blind separation of digital signals based on elitist genetic algorithms is presented in this paper. Contrast function, consisting in a weighted sum of high order statistics measures (cumulants of different orders), plays the role of genetic fitness function, and also guide the genetic algorithm by a Gauss-Newton adaptation applied to the genetic population, that reduces the search space and provide faster convergence rate. The use of elitism assures the convergence of the algorithm. Several experiments were conducted on digital signals and mixing models, and the high amount of simulations derived from them provided the best combination of the constant parameters in terms of separation accuracy and convergence rate. In this sense, we also achieve a robust blind source separation method that efficiently adapts to the statistical nature of the mixing signals, within a low population of the genetic algorithm.

关键词： Gauss-Newton adaptation Gaussian processes blind separation blind source separation blind source separation method contrast function convergence convergence rate digital signal digital signals elitist genetic algorithm genetic algorithms genetic fitness function genetic population higher order statistic mixing model mixing signal search problems search space

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Adaptive elitist genetic algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

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IEEE ACCESS 2021年 9卷 16661-16671页

作者： Zhu, Yanfei Lee, Kwang Y. Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U.S. Postal Service system.

关键词： elitist genetic algorithm electric vehicle routing neighbor routing EVRPTW

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Optimal Reservoir Release Policy Considering Heterogeneity of Command Area by elitist genetic algorithm

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WATER RESOURCES MANAGEMENT 2011年第14期25卷 3863-3881页

作者： Garudkar, A. S. Rastogi, A. K. Eldho, T. I. Gorantiwar, S. D. Indian Inst Technol Dept Civil Engg Bombay 400076 Maharashtra India MPKV Dept Irrigat & Drainage Engg Ahmednagar Maharashtra India

A number of models with conventional optimization techniques have been developed for optimization of reservoir water release policies. However these models are not able to consider the heterogeneity in the command area of the reservoir appropriately, due to non linear nature of the processes involved. The optimization model based on genetic algorithm (GA) can deal with the non linearity due to its inherent ability to consider complex simulation model as evaluation function for optimization. GA based models available in literature generally minimize the water deficits and do not optimize the total net benefits through optimal reservoir release policies. The present study focuses on optimum releases from the reservoir considering heterogeneity of the command area and responses of the command area to the releases instead of minimizing only the reservoir storage volumes. An optimization model has been developed for the reservoir releases based on elitist GA approach considering the heterogeneity of the command area. The developed model was applied to Waghad irrigation project in upper Godavari basin of Maharashtra, India. The results showed that 19% increase in the total net benefits could be possible by adopting the proposed water release policy over the present practice keeping same distribution of area under different crops. The model presented in this study can also optimize the crop area under irrigation. It is found that irrigated area can be increased to 50% of ICA (Irrigable Command Area) from the existing 23% with resulting addition to total net benefits by 31%. The effect of adopting the proposed irrigation schedule and increased irrigation areas would be to increase the net benefits to existing farmers.

关键词： elitist genetic algorithm Reservoir operating policies Heterogeneity of command area Crop growth model Waghad irrigation project

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Optimizing Template for Lookup-Table Inverse Halftoning using elitist genetic algorithm

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IEEE SIGNAL PROCESSING LETTERS 2015年第1期22卷 71-75页

作者： Wen, Zhi-Qiang Lu, Yong-Le Zeng, Zhi-Gao Zhu, Wen-Qiu Ai, Jun-Hua Hunan Univ Technol Sch Comp & Commun Zhuzhou 412007 Hunan Peoples R China

A template optimization method based on elitist genetic algorithm was proposed for lookup-table inverse halftoning. A mathematical model with constraint conditions was built to describe the template optimization problem. We solved this optimization problem by using elitist genetic algorithm and designed the details about encoding and decoding scheme, selection and reproduce, crossover, mutation, elitist strategy and fitness function according to the proposed optimization model. In experiments, we demonstrated the performance on Floyd-Steinberg error diffusion, Jarvis-Judice error diffusion, cluster dither, Bayer disperse dither and dot diffusion halftone images. According to our experiment study, our method approaches to the optimal result closer than the greedy algorithm and simulated annealing do. We suggested L = 10 on cluster dither images but L = 5 on other four kinds of halftone images.

关键词： elitist genetic algorithm inverse halftoning lookup table template optimization

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Estimation of the amplification properties of soil through HVSR inversion based on an elitist genetic algorithm

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EARTH SCIENCE INFORMATICS 2022年第4期15卷 2319-2334页

作者： Kafadar, Ozkan Imamoglu, Cagri Kocaeli Univ Dept Construct Technol TR-41140 Kocaeli Turkey Jura Madencilik Muhendislik Musavirlik San & Tic TR-06530 Ankara Turkey

The horizontal-to-vertical spectral ratio method is one of the most commonly utilized techniques to estimate the site response. This method is frequently preferred for practical calculation of the dynamic properties of soil layers. Recently, the popularity of this technique has been increasing thanks to the methods developed to obtain the shear wave velocity profile from the horizontal-to-vertical spectral ratio. In this study, a MATLAB-based graphical user interface has been developed for inversion and forward calculation of the horizontal-to-vertical spectral ratio. This code uses the equivalent linear approach based on the viscoelastic Kelvin-Voigt model to compute the theoretical site response of the horizontally stratified soil layers. Furthermore, the developed graphical user interface can easily estimate the dynamic parameters such as thickness, shear wave velocity, density and damping ratio of the soil layers through an elitist genetic algorithm, and thereby obtain the shear wave velocity profiles. The reliability of the developed algorithm has been tested using the synthetic and real datasets. The results from the real data examples have been compared with those from previous studies and the satisfactory results have been obtained.

关键词： Forward modeling elitist genetic algorithm Equivalent linear approach HVSR inversion Shear wave velocity profile

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An Approach for Mutation Testing Using elitist genetic algorithm

An Approach for Mutation Testing Using Elitist Genetic Algor...

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Mishra, K. K. Tiwari, Shailesh Kumar, Anoj Misra, A. K. MNNIT Dept Comp Sci & Engn Allahabad Uttar Pradesh India

ISBN: (纸本)9781424455379

Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing methodologies. In order to reduce the cost of mutation testing, efficient test cases are generated that reveal faults and kill mutants. In this paper, we develop a new strategy for generating efficient test input data in the context of mutation testing.

关键词： Mutation Testing genetic algorithm elitist genetic algorithm

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EXAMINING THE RELATIONSHIP BETWEEN algorithm STOPPING CRITERIA AND PERFORMANCE USING elitist genetic algorithm

EXAMINING THE RELATIONSHIP BETWEEN ALGORITHM STOPPING CRITER...

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2010 Winter Simulation Conference

作者： Kim, Jin-Lee Calif State Univ Long Beach Long Beach CA 90840 USA

ISBN: (纸本)9781424498642

A major disadvantage of using a genetic algorithm for solving a complex problem is that it requires a relatively large amount of computational time to search for the solution space before the solution is finally attained. Thus, it is necessary to identify the tradeoff between the algorithm stopping criteria and the algorithm performance. As an effort of determining the tradeoff, this paper examines the relationship between the algorithm performance and algorithm stopping criteria. Two algorithm stopping criteria, such as the different numbers of unique schedules and the number of generations, are used, while existing studies employ the number of generations as a sole stopping condition. elitist genetic algorithm is used to solve 30 projects having 30-Activity with four renewable resources for statistical analysis. The relationships are presented by comparing means for algorithm performance measures, which include the fitness values, total algorithm runtime in millisecond, and the flatline starting generation number.

关键词： algorithm performance algorithm stopping criteria elitist genetic algorithm genetic algorithms

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A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning algorithm along with elitist genetic algorithm (EGA) and Random Forest-Based Hybrid Feature Selection

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ENERGIES 2023年第2期16卷 867页

作者： Srivastava, Ankit Kumar Pandey, Ajay Shekhar Abou Houran, Mohamad Kumar, Varun Kumar, Dinesh Tripathi, Saurabh Mani Gangatharan, Sivasankar Elavarasan, Rajvikram Madurai Dr Rammanohar Lohia Avadh Univ Elect Engn Dept Ayodhya 224001 India Kamla Nehru Inst Technol Dept Elect Engn Sultanpur 228118 India Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Thiagarajar Coll Engn Elect & Elect Engn Dept Madurai 625015 India Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets.

关键词： confidence interval elitist genetic algorithm feature selection short-term load forecasting M5P forecaster machine learning

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TIME-OPTIMAL MOTION PLANNING FOR ROBOT MANIPULATORS BASED ON elitist genetic algorithm

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2017年第4期32卷 396-405页

作者： Liu, Yi Cong, Ming Dong, Hang Liu, Dong Du, Yu Dalian Univ Technol Sch Mech Engn Dalian Liaoning Peoples R China Univ British Columbia Dept Mech Engn 2329 West Mall Vancouver BC V6T 1Z4 Canada

In this paper, a new method for planning high smooth and time-optimal motion for robot manipulators is presented. In our method, the angular velocity, acceleration and jerk of each axis are planned and limited to avoid robot strenuous vibration. Quintic spline curve is used to make the kinematic parameters configurable for the end-point in the task status of robot manipulators. Kinematics solution in the Cartesian space is provided to guarantee the controllability and accuracy for the end-effector. A highlight of the proposed method is that the minimum-time issue can be successfully solved by using the elitist genetic algorithm with a rather higher convergence rate. Experiments were made to demonstrate the effectiveness of the proposed method.

关键词： Motion planning kinematics quintic spline robot manipulators minimum-time optimization elitist genetic algorithm

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