Journal articles: 'Genetic improvement of software'

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Langdon, William B., Brian Yee Hong Lam, Marc Modat, Justyna Petke, and Mark Harman. "Genetic improvement of GPU software." Genetic Programming and Evolvable Machines 18, no. 1 (July 25, 2016): 5–44. http://dx.doi.org/10.1007/s10710-016-9273-9.

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Petke, Justyna, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. "Genetic Improvement of Software: A Comprehensive Survey." IEEE Transactions on Evolutionary Computation 22, no. 3 (June 2018): 415–32. http://dx.doi.org/10.1109/tevc.2017.2693219.

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Brownlee, Alexander E. I. "Genetic Improvement @ ICSE 2021." ACM SIGSOFT Software Engineering Notes 46, no. 4 (October 27, 2021): 28–30. http://dx.doi.org/10.1145/3485952.3485960.

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Following Dr. Stephanie Forrest of Arizona State University's keynote presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021). Topics included a growing range of target systems and appli- cations, algorithmic improvements, wide-ranging questions about how other elds (especially evolutionary computation) can inform advances in GI, and about how GI is 'branded' to other disciplines. We give a personal perspective on the workshop's proceedings, the discussions that took place, and resulting prospective directions for future research.

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Langdon, W. B. "Big data driven genetic improvement for maintenance of legacy software systems." ACM SIGEVOlution 12, no. 3 (January 28, 2020): 6–9. http://dx.doi.org/10.1145/3381343.3381345.

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Jain, Rachna, and Arun Sharma. "ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS." Journal of Engineering Research [TJER] 16, no. 1 (May 9, 2019): 11. http://dx.doi.org/10.24200/tjer.vol16iss1pp11-17.

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The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms. A genetic algorithm has been explored for predicting software reliability. One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions.

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López-López, Víctor R., Leonardo Trujillo, and Pierrick Legrand. "Applying genetic improvement to a genetic programming library in C++." Soft Computing 23, no. 22 (December 19, 2018): 11593–609. http://dx.doi.org/10.1007/s00500-018-03705-6.

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Petke, Justyna, Mark Harman, William B. Langdon, and Westley Weimer. "Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation." IEEE Transactions on Software Engineering 44, no. 6 (June 1, 2018): 574–94. http://dx.doi.org/10.1109/tse.2017.2702606.

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Reza Mashinchi, M., and Ali Selamat. "An improvement on genetic-based learning method for fuzzy artificial neural networks." Applied Soft Computing 9, no. 4 (September 2009): 1208–16. http://dx.doi.org/10.1016/j.asoc.2009.03.011.

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Vijayalakshmi, K., N. Ramaraj, and R. Amuthakkannan. "Improvement of component selection process using Genetic Algorithm for Component-Based Software Development." International Journal of Information Systems and Change Management 3, no. 1 (2008): 63. http://dx.doi.org/10.1504/ijiscm.2008.019289.

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Hafiiak, A., E. Borodina, and A. Diachenko-Bohun. "APPLICATION OF GENETIC PROGRAMMING TOOLS AS A MEANS OF SOLVING OPTIMIZATION PROBLEMS." Системи управління, навігації та зв’язку. Збірник наукових праць 6, no. 52 (December 13, 2018): 58–60. http://dx.doi.org/10.26906/sunz.2018.6.058.

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Purpose. The article is devoted to the problem of practical application of genetic programming tools as a means of solving optimization problems and the use of genetic programming in various fields of activity. It is established that the evolution of genetic programming is directly related to the development of the genetic algorithm, it is also determined that with the passage of time a significant improvement in genetic programming has occurred. Since the advent of the genetic algorithm, many modifications and software implementations have appeared. This in turn led to the implementation of the genetic algorithm toolkit in software products, namely: specialized software, applications for mathematical and analytical packages, frameworks and libraries. The article reveals the significant impact of genetic programming in the areas of: quantum computing, electrical circuit design, etc. Not only advantages, but also disadvantages are considered, attention is also paid to methods of eliminating deficiencies by improving optimization methods and applying a genetic algorithm. Results. The analysis of the main directions of the practical use of genetic programming is carried out and tasks that can be effectively solved using this toolkit are outlined. Scientific novelty. It was determined that the improvement of optimization methods and the expansion of the use of genetic algorithms, stimulates the appearance of such software products on the market, simplifies the structure of software tools, designs the interface for working with a specific commercial user community, simplifies the command language, which allows the use of genetic programming tools circle of users with different levels of training.

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Huh, Jun-Ho, Jimin Hwa, and Yeong-Seok Seo. "Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing." Sustainability 12, no. 6 (March 11, 2020): 2177. http://dx.doi.org/10.3390/su12062177.

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A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

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Guo, Mei Ni. "Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem." Applied Mechanics and Materials 365-366 (August 2013): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.194.

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mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

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Lempa, Paweł, Edward Lisowski, Fumito Masui, Grzegorz Filo, Michal Ptaszynski, Mariusz Domagała, and Joanna Fabiś-Domagała. "Quality Improvement of a Gear Transmission by Means of Genetic Algorithm." Quality Production Improvement - QPI 1, no. 1 (July 1, 2019): 386–93. http://dx.doi.org/10.2478/cqpi-2019-0052.

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Abstract The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.

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Zakir Khan, Muhammad, Rashid Naseem, Aamir Anwar, Ijaz ul-Haq, Saddam Hussain, Roobaea Alroobaea, Syed Sajid Ullah, and Fazlullah Umar. "An Enhanced Multifactor Multiobjective Approach for Software Modularization." Mathematical Problems in Engineering 2022 (June 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/7960610.

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Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

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White, Brian T. "The Virtual Genetics Lab II: Improvements to a Freely Available Software Simulation of Genetics." American Biology Teacher 74, no. 5 (May 1, 2012): 336–37. http://dx.doi.org/10.1525/abt.2012.74.5.9.

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The Virtual Genetics Lab II (VGLII) is an improved version of the highly successful genetics simulation software, the Virtual Genetics Lab (VGL). The software allows students to use the techniques of genetic analysis to design crosses and interpret data to solve realistic genetics problems involving a hypothetical diploid insect. This is a brief outline of the program and its new features; details are available at http://intro.bio.umb.edu/vgl/.

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Floss, Paulo Alfonso, Carlos André Stuepp, Ivar Wendling, Vânia Beatriz Cipriani, and Cristiane Aparecida Fioravante Reis. "Genetic improvement of Ilex paraguariensis in western Santa Catarina State: estimate of phenotypic and genetic parameters." Research, Society and Development 11, no. 3 (February 9, 2022): e1711326084. http://dx.doi.org/10.33448/rsd-v11i3.26084.

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An especially important species in South America, yerba mate (Ilex paraguariensis A.St.-Hil) is traditionally consumed as tea, chimarrão and tererê. Due to the formation of mate plantations with low productivity and high heterogeneity, genetic improvement actions have been conducted as a way to generate more productive cultivars of this species. In this sense, the present study aimed to estimate phenotypic and genetic parameters of yerba mate production traits earlier in provenance and progeny tests, implanted in the municipalities of Chapecó and Guatambu, in the western region of Santa Catarina State (South America). In each environment, 55 open pollinated yerba mate progenies were evaluated in a randomized complete block design, with five replications and three plants per plot. The traits measured at two years old were: height and crown diameter (m) and at three years were: visually estimated commercial biomass, weighed commercial biomass and thick branch mass (kg). The data obtained was analyzed using the genetic-statistical Selegen software. The results obtained support that there is genetic variability in the open pollinated yerba mate progenies evaluated in Santa Catarina, with possible gains by selecting the best parent trees for future production of improved seeds and for cloning.

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Badarudin, I. M., A. B. M. Sultan, M. N. Sulaiman, A. Mamat, and M. T. M. Mohamed. "A Framework of Genetic Algorithm Improvement for Optimal Block Division in Lining Layout Planning." Journal of Artificial Intelligence 5, no. 2 (March 15, 2012): 64–75. http://dx.doi.org/10.3923/jai.2012.64.75.

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Kuckling, Jonas, Thomas Stützle, and Mauro Birattari. "Iterative improvement in the automatic modular design of robot swarms." PeerJ Computer Science 6 (December 7, 2020): e322. http://dx.doi.org/10.7717/peerj-cs.322.

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Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular design of control software for robot swarms. In particular, we investigate the optimization of two control architectures: finite-state machines and behavior trees. Finite state machines are a common choice for the control architecture in swarm robotics whereas behavior trees have received less attention so far. We compare three different optimization techniques: iterative improvement, Iterated F-race, and a hybridization of Iterated F-race and iterative improvement. For reference, we include in our study also (i) a design method in which behavior trees are optimized via genetic programming and (ii) EvoStick, a yardstick implementation of the neuro-evolutionary swarm robotics approach. The results indicate that iterative improvement is a viable optimization algorithm in the automatic modular design of control software for robot swarms.

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Du, Peng Dong, and Yan Hua Chu. "The Improved Genetic Algorithms Apply on Parameter Estimation of Two Parameters Logistic Model on Item Response Theory." Advanced Materials Research 756-759 (September 2013): 2620–24. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2620.

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Based on the item response theory 2PLM parameter estimation method and genetic algorithm in Detailed exploration, put forward a kind of 2PLM based on genetic algorithm parameter estimation method, and the corresponding algorithm program for different item parameter estimation. On the basis of genetic coding, genetic analysis and reference, proposed to the operators of genetic improvement strategy and algorithm to accelerate the convergence of strategy, wove algorithm verification procedures and foreign popular BILOG software were compared, the results showed that, in a certain range of error, the proposed algorithm can converge to the optimal solution.

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Shivakumar, Shailesh Kumar. "Software Estimation Framework for Digital Enhancements and Maintenance Projects." International Journal of Project Management and Productivity Assessment 8, no. 2 (July 2020): 81–96. http://dx.doi.org/10.4018/ijpmpa.2020070105.

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Software enhancements and the maintenance phase is generally the crucial phase of a software application lifecycle. The enhancements and maintenance consume about 20% of the overall software lifecycle effort. Enhancement and maintenance phase of modern digital projects involves many activities such as incident management, application enhancements, generic maintenance, quality improvements such as automation, preventive maintenance, continuous improvement, and such. State-of the-art estimation models and frameworks fall short of factoring all the dynamics involved in the enhancements and maintenance phase. The article proposes a digital project maintenance estimation framework to estimate various activities of a digital maintenance project. The proposed estimation framework provides comprehensive coverage of maintenance activities including incident management, application enhancements, generic maintenance, and quality improvements. The proposed estimation framework was used to predict effort estimate of 5 digital maintenance projects with MMRE of 0.255 and predicted (0.3) of 80%.

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ANTICH, DMYTRO, and GALINA RADELCHUK. "IMPROVEMENT OF SOFTWARE SYSTEMS FAULT TOLERANCE ENSURING ALGORITHMS." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 299, no. 4 (October 2021): 54–58. http://dx.doi.org/10.31891/2307-5732-2021-299-4-54-58.

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The study investigates the concepts of fault tolerance and methods of system responses to failures. The study is based on the research of modern resiliency patterns and common approaches of reaction to failures. During the research, the common unresolved issues with modern resiliency and fault tolerance approaches were defined. The study improved the method of the system response to failures by designing a comprehensive solution that includes refinement and expansion of classical patterns of fault tolerance as a proposal to resolve common problems. The new solution of fault tolerance is based on the combination of basic monitoring approaches, load balancing approaches, circuit breaker pattern, and re-designing of the sharding pattern to be applicable not only for databases but also for modern applications. The new solution is based on an automatic decision-making expert system, which based on anonymous data saved by the monitoring layer decides the root cause of the issue and validates which scenario is applicable for the current situation. Based on the decision system can either enable a user and load balancing approaches by isolating harmful users using improved sharding and load-balancing solutions or enable a circuit breaker to temporarily disable the faulty features. The new method of resiliency is supposed to prevent and reduce more errors compared to the existing solutions in the domain of fault tolerance and resiliency, thus the efficiency of the new approach is higher. The expediency and urgency of designing a new method of fault tolerance are substantiated by expressing the importance of resolving existing problems. Improved methods of automatic response and failure prevention, which allowed to reduce the number of errors that occur in the system compared to existing solutions in resiliency and fault tolerance.

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Zeng, Yifu, Yantao Zhou, and Fei Zheng. "Data skyline query protocol based on parallel genetic improvement decision tree." Journal of Supercomputing 76, no. 2 (September 7, 2018): 1116–27. http://dx.doi.org/10.1007/s11227-018-2593-1.

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C. "End to End Delay Improvement in Heterogeneous Multicast Network using Genetic Optimization." Journal of Computer Science 8, no. 9 (September 1, 2012): 1514–19. http://dx.doi.org/10.3844/jcssp.2012.1514.1519.

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Kogiso, N., L. T. Watson, Z. Gürdal, and R. T. Haftka. "Genetic algorithms with local improvement for composite laminate design." Structural Optimization 7, no. 4 (June 1994): 207–18. http://dx.doi.org/10.1007/bf01743714.

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Khanam, M., MS Kabiraj, MM Rashid, and SA Raffi. "Evaluation of genetic variability and trait association for yield improvement of Lentil." Progressive Agriculture 32, no. 2 (March 3, 2022): 107–16. http://dx.doi.org/10.3329/pa.v32i2.58395.

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Lentil is a nutritious food and one of the world's oldest domesticated legumes. The present studyevaluatedthe nature and magnitude of variability, heritability, genetic advance and association among the yield and yield contributing traits in 24 lentils (Lens culinaris M.) genotypes. From the 13 traits considered, phenotypic coefficients of variation (PCV) were found higher than genotypic coefficient of variation (GCV) which indicates less effect of the environment for the expression of traits studied. Seed weight per plants demonstrated the highest PCV and GCV (60.26 & 59.87) followed by number of seeds per plant (49.14 & 48.97) and number of pods per plant (48.58 & 47.95, respectively). Most of the traits showed high heritability as days to maturity exhibited the highest heritability (99.96%) followed by number of seeds per plant (99.33%). Genetic advance as percent of mean was higher for seed weight per plant (122.58%) and number of seeds per plant (100.56%). Among the traits, number of seeds per plant (0.95& 0.94) and number of pods per plant (0.94 & 0.92) showed positive and significant correlation with seed weight per plant at both phenotypic and genotypic correlation. Consequently, path analysis revealed positive and direct effect of number of pods per plant (0.310&372) and number of seeds per plant (0.770&0.659) on seed weight per plant in both genotypic and phenotypic level, respectively. Based on the genetic analysis of 13 characters, number of pods per plant, number of seeds per plant,1000-seed weight and seed weight per plant were found as the most superior traits can be used in hybridization program for the development of high yielding lentil genotypes. Progressive Agriculture 32 (2): 107-116, 2021

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Morante, R., F. Goyache, A. Burgos, I. Cervantes, M. A. Pérez-Cabal, and J. P. Gutiérrez. "Genetic improvement for alpaca fibre production in the Peruvian Altiplano: the Pacomarca experience." Animal Genetic Resources Information 45 (October 2009): 37–43. http://dx.doi.org/10.1017/s1014233909990307.

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SummaryPacomarca is an experimental ranch founded by the INCA group to act as a selection nucleus from which basic genetic improvement of alpaca fibre can spread throughout the rural communities in the Peruvian Altiplano. State-of-art techniques in animal science, such as performance recording or assisted reproduction including embryo transfer, are applied to demonstrate their usefulness in the Altiplano conditions. Pacomarca has developed useful software (Paco Pro) to carry out the integral processing of production and reproduction data. Mating is carried out individually, and gestation is diagnosed via ultrasound. Breeding values estimated from a modern genetic evaluation are used for selection, and embryo transfer is applied to increase the selection intensity. However, the objective of Pacomarca goes beyond, extending its advances to the small rural communities. Training courses for farmers are organised while searching for new ways of improving the performance of alpacas both technically and scientifically.

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Salim, Khiat, Rahal Sidi Ahmed Hebri, and Senaï Besma. "Classification Predictive Maintenance Using XGboost with Genetic Algorithm." Revue d'Intelligence Artificielle 36, no. 6 (December 31, 2022): 833–45. http://dx.doi.org/10.18280/ria.360603.

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This study develops a condition classification system of compressor 103J and water pump systems which are key equipment in the ammonia production line, hence the monitoring of these two very important machines. In recent years, there are many good intelligent machine learning algorithms and XGboost is one of them. However, it contains many parameters and classification performance of the model will be greatly affected by the selection of parameters and their combination technique. In this paper, XGboost algorithm is combined with the genetic algorithm, called GA-XGboost, in order to find the best hyper parameters of classifiers which makes the classifier more efficient and ensures the proper functioning of compressor 103J and water pump systems. Experiments show that GA-XGboost algorithm has improved the accuracy of classification in the compressor 103J and the water pump dataset compared with other machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF) and AdaBoost. Also experiments demonstrate the improvement of the GA-XGboost algorithm by the combination of different selection and crossover operators of the genetic algorithm.

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Kouchakpour, Peyman, Anthony Zaknich, and Thomas Bräunl. "A survey and taxonomy of performance improvement of canonical genetic programming." Knowledge and Information Systems 21, no. 1 (December 12, 2008): 1–39. http://dx.doi.org/10.1007/s10115-008-0184-9.

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Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.32.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.

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Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.27.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.

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Latifi, Meysam, and Ali Mohammadi. "Analysis of genetic parameters and genetic trends for early growth traits in Iranian Afshari sheep." Biotehnologija u stocarstvu 34, no. 3 (2018): 289–301. http://dx.doi.org/10.2298/bah1803289l.

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The purpose of the present study was estimation of genetic parameters and genetic trends of early growth traits using Bayesian approach by Gibbs3f90 software in Iranian Afshari sheep. The data set [birth weight (BW), weaning weight (WW) and pre-weaning daily weight gain (PWDG)] were collected during the period 1999 to 2010 from Agriculture Jahad of Zanjan province, Iran. The fitted fixed effects were herd-year-season as interactions, sex (male, female), birth type (single, multiple) and age of dam. Based on Derivative Information Criteria (DIC), for studied traits the most appropriate model was determined. Therefore, based on the most appropriate fitted model, the direct additive heritabilities estimate for BW, WW and PWDG were 0.32?0.02, 0.05?0.01 and 0.24?0.02, respectively. The estimates of maternal heritabilities were 0.17?0.04, 0.07?0.02 and 0.12?0.05 and total heritabilities 0.11?0.05, 0.08?0.02 and 0.08?0.03 for BW, WW and PWDG, respectively. Direct genetic trends were positive for all traits but only significant for BW 0.75?0.31 g/year (P <0.05). Also, maternal genetic trends were for all traits negative and was significant for BW -0.63?0.27 g/year (P <0.05). The moderate estimates of heritabilities for early growth traits indicate that in Afshari sheep faster genetic improvement through selection is possible for these traits. Furthermore, the results genetic trends in this current study indicated that genetic improvement through selection is suitable only for BW in Afshari sheep.

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Liu, Yu, Feng Qin Wang, and Xiu Li Zhao. "An Approach to Generate Test Cases by Multi-Path Based on Genetic Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3976–79. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3976.

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Software testing is important to ensure the quality and reliability of the software.The improvement on the automation of test case generation is the entire key to improve the automation of the testing process.It helps a lot in the generation of test cases to construct multi-path model.It is based on genetic algorithm with three parts which are the test environment construction, the genetic algorithms and the operating environment.It’s feasibility and efficiency is verified by triangle classification procedures.

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Gulayeva, N. M., and S. A. Yaremko. "EXPERIMENTAL ANALYSIS OF MULTINATIONAL GENETIC ALGORITHM AND ITS MODIFICATIONS." Radio Electronics, Computer Science, Control, no. 2 (July 3, 2021): 71–83. http://dx.doi.org/10.15588/1607-3274-2021-2-8.

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Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.

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Shah, Syed Muhammad Saqlain, Rehan Farooq, Abdullah Alharbi, Hashem Alyami, Islam Zada, and Faiz Ali Shah. "Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework." Scientific Programming 2022 (April 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/3708422.

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It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.

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Shah, Syed Muhammad Saqlain, Rehan Farooq, Abdullah Alharbi, Hashem Alyami, Islam Zada, and Faiz Ali Shah. "Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework." Scientific Programming 2022 (April 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/3708422.

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It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.

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Carvalho, N. S., D. S. Daltro, J. D. Machado, E. V. Camargo, J. C. C. Panetto, and J. A. Cobuci. "Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 73, no. 4 (August 2021): 938–48. http://dx.doi.org/10.1590/1678-4162-12341.

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ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

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Fernandes, Ana Clara G., Nermy R. Valadares, Clóvis Henrique O. Rodrigues, Rayane A. Alves, Lis Lorena M. Guedes, Jailson R. Magalhães, Rafael B. da Silva, Luan S. de P. Gomes, and Alcinei M. Azevedo. "Feasibility of computational vision in the genetic improvement of sweet potato root production." Horticultura Brasileira 40, no. 4 (December 2022): 378–83. http://dx.doi.org/10.1590/s0102-0536-20220405.

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ABSTRACT The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process.

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Grecu, Valentin, Radu-Ilie-Gabriel Ciobotea, and Adrian Florea. "Software Application for Organizational Sustainability Performance Assessment." Sustainability 12, no. 11 (May 29, 2020): 4435. http://dx.doi.org/10.3390/su12114435.

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Sustainability performance assessment is a challenge for many companies due to the heterogeneity of indicators and the lack of a standardized reporting framework. This paper describes a software solution that simplifies the sustainability reporting process and is useful for decisions concerning sustainable management. We analyzed various indicators from public sustainability reports of five companies and obtained some relevant results using the tool that we developed based on mathematic algorithms and an aggregation model of different indicators. The software application calculates a Global Sustainability Index based on the proposed model of the sustainable organization described in this paper. An optimal solution is very rare in the transition towards the sustainable organization and compromises are required most frequently between environmental, economic and social aspects on the one hand and the expectations of the stakeholders on the other hand. The proposed tool helps users to cope with these challenges and takes into consideration that information is not always available and precise. Another feature offered by the tool is that besides simplifying sustainability performance assessment, it highlights low performance indicators and offers suggestions for improvement based on a genetic algorithm.

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Veerapen, Nadarajen, and Gabriela Ochoa. "Visualising the global structure of search landscapes: genetic improvement as a case study." Genetic Programming and Evolvable Machines 19, no. 3 (August 6, 2018): 317–49. http://dx.doi.org/10.1007/s10710-018-9328-1.

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ROY, KAUSHIK, and PRABIR BHATTACHARYA. "IMPROVEMENT OF IRIS RECOGNITION PERFORMANCE USING REGION-BASED ACTIVE CONTOURS, GENETIC ALGORITHMS AND SVMs." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (December 2010): 1209–36. http://dx.doi.org/10.1142/s0218001410008421.

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Most existing iris recognition algorithms focus on the processing and recognition of the ideal iris images that are acquired in a controlled environment. In this paper, we process the nonideal iris images that are captured in an unconstrained situation and are affected severely by gaze deviation, eyelids and eyelashes occlusions, nonuniform intensity, motion blur, reflections, etc. The proposed iris recognition algorithm has three novelties as compared to the previous works; firstly, we deploy a region-based active contour model to segment a nonideal iris image with intensity inhomogeneity; secondly, genetic algorithms (GAs) are deployed to select the subset of informative texture features without compromising the recognition accuracy; Thirdly, to speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The verification and identification performance of the proposed scheme is validated on three challenging iris image datasets, namely, the ICE 2005, the WVU Nonideal, and the UBIRIS Version 1.

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DUROSARO, S. O., B. T. OSHINOWO, A. C. AKPOJO, L. T. OLUYOMBO, I. C. NWOSU, O. S. IYASERE, E. V. IKPEME, and M. O. OZOJE. "A PRELIMINARY COMPARISON OF MITOCHONDRIAL D-LOOP REGION OF FUNAAB ALPHA AND NIGERIAN INDIGENOUS CHICKENS." Journal of Agricultural Science and Environment 20, no. 1 (December 2, 2021): 1–11. http://dx.doi.org/10.51406/jagse.v20i1.2090.

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Nigerian indigenous chickens possess immunity from endemic diseases and have a better survival rate than commercial hybrid strains under local production conditions. FUNAAB Alpha chicken was developed by improving Nigerian indigenous chickens through crossbreeding and selection. This study compared the mitochondrial d-loop of FUNAAB Alpha and Nigerian indigenous chickens to check likely genetic erosion and loss of diversity in development of FUNAAB Alpha breed. Blood samples were collected from Nigerian indigenous (n=23) and FUNAAB Alpha (n=20) chickens sampled from farms and houses in Ogun state, Nigeria. The Hypervariable 1 (HV1) of the mitochondrial d-loop region was amplified and sequenced. Single nucleotide polymorphisms present in HV1 of chickens were identified using Clustal W. Genetic diversity of the region was determined using DnaSp v5 while selective forces acting on the chickens were predicted using HyPhy software implemented inside MEGA 6 software. Phylogenetic relationship among FUNAAB Alpha, Nigerian indigenous and other chicken breeds was determined using MEGA 6 software. Five polymorphisms were identified in FUNAAB Alpha chickens while twelve were identified in Nigerian indigenous chickens. All the polymorphisms identified in FUNAAB Alpha chickens were also observed in Nigerian indigenous chickens while seven polymorphisms were unique to Nigerian indigenous chickens. Higher diversity indices were observed in Nigerian indigenous chickens (number of haplotype: 4; haplotype diversity: 0.743±0.012; nucleotide diversity: 0.014±0.0013 and average number of nucleotide differences: 4.332) compared with FUNAAB Alpha chickens (number of haplotype: 2; haplotype diversity: 0.485±0.001; nucleotide diversity: 0.008±0.0001 and average number of nucleotide differences: 2.424). Positive selective forces were acting on FUNAAB Alpha chickens while negative selective forces were acting on Nigerian indigenous chickens. Phylogenetic analysis revealed that FUNAAB Alpha chickens clustered with Nigerian indigenous and South American chickens. It can be concluded that there was likely genetic erosion and loss of diversity in development of FUNAAB Alpha breed. Breeding programmes aimed at improvement of genetic diversity and reduction of genetic erosion should be applied in subsequent improvement of FUNAAB Alpha chickens.

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Souleymane, Oumarou, Batieno Teyioué Benoit Joseph, Baboucarr Manneh, Kwadwo Ofori, and Eric Danquah. "Genetic Diversity Assessment of Four Rice Varieties using SNP Markers." European Journal of Engineering Research and Science 2, no. 12 (December 16, 2017): 17. http://dx.doi.org/10.24018/ejers.2017.2.12.504.

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Markers assisted selection is a tool for improving the speed and efficiency of crop improvement. The identification of SNPs within the target genomic region of a QTL is necessary for efficient breeding of quantitative traits such as salt tolerance. The study was conducted to characterize parental lines using SNP markers and to identify the polymorphic SNPs for salt tolerance in QTL study of offspring. Four rice lines were evaluated under five levels of salt conditions including the control. The experimental design was a split-plot with two replications. Leaf samples were collected from four rice lines at heading stage and sent to LGC Genomics laboratory for genotyping using rice SNP platform. DNA extraction and SNP genotyping were performed using an internal protocol. The phenotypic data recorded were the visual scoring, tiller number, green leaf number, panicle number, panicle weight and dry weight. Phenotypic data were analyzed using SAS 9.2 software. The genotypic data were analyzed using MEGA6 and GGT2 software. Significant diversity was revealed among lines concerning all the phenotypic traits. Significant diversity, divergence and substitution pattern were observed among lines. 200 markers out of 1896 were polymorphic and selected for the next step.

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Souleymane, Oumarou, Batieno Teyioué Benoit Joseph, Baboucarr Manneh, Kwadwo Ofori, and Eric Danquah. "Genetic Diversity Assessment of Four Rice Varieties using SNP Markers." European Journal of Engineering and Technology Research 2, no. 12 (December 16, 2017): 17–22. http://dx.doi.org/10.24018/ejeng.2017.2.12.504.

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Markers assisted selection is a tool for improving the speed and efficiency of crop improvement. The identification of SNPs within the target genomic region of a QTL is necessary for efficient breeding of quantitative traits such as salt tolerance. The study was conducted to characterize parental lines using SNP markers and to identify the polymorphic SNPs for salt tolerance in QTL study of offspring. Four rice lines were evaluated under five levels of salt conditions including the control. The experimental design was a split-plot with two replications. Leaf samples were collected from four rice lines at heading stage and sent to LGC Genomics laboratory for genotyping using rice SNP platform. DNA extraction and SNP genotyping were performed using an internal protocol. The phenotypic data recorded were the visual scoring, tiller number, green leaf number, panicle number, panicle weight and dry weight. Phenotypic data were analyzed using SAS 9.2 software. The genotypic data were analyzed using MEGA6 and GGT2 software. Significant diversity was revealed among lines concerning all the phenotypic traits. Significant diversity, divergence and substitution pattern were observed among lines. 200 markers out of 1896 were polymorphic and selected for the next step.

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KHARMA, N., T. KOWALIW, E. CLEMENT, C. JENSEN, A. YOUSSEF, and J. YAO. "PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 06 (September 2004): 1039–56. http://dx.doi.org/10.1142/s0218001404003587.

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We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet utilizes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.

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Schmiedt, Marius, Ping He, and Stephan Rinderknecht. "Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions." Applied Sciences 12, no. 20 (October 12, 2022): 10283. http://dx.doi.org/10.3390/app122010283.

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Vehicles with dual clutch transmissions (DCT) are well known for their comfortable drivability since gear shifts can be performed jerklessly. The ability of blending the torque during gear shifts from one clutch to the other, making the type of automated transmission a perfect alternative to torque converters, which also comes with a higher efficiency. Nevertheless, DCT also have some drawbacks. The actuation of two clutches requires an immense control effort, which is handled in the implementation of a wide range of software functions on the transmission control unit (TCU). These usually contain control parameters, which makes the behavior adaptable to different vehicle and engine platforms. The adaption of these parameters is called calibration, which is usually an iterative time-consuming process. The calibration of the embedded software solutions in control units is a widely known problem in the automotive industry. The calibration of any vehicle subsystem (e.g., engine, transmission, suspension, driver assistance systems for autonomous driving, etc.) requires costly test trips in different ambient conditions. To reduce the calibration effort and the accompanying use of professionals, several approaches to automize the calibration process are proposed. Due to the fact that a solution is desired which can optimize different calibration problems, a generic metaheuristic approach is aimed. Regardless, the scope of the current research is the optimization of the launch behavior for vehicles equipped with DCT since, particularly at low speeds, the transmission behavior must meet the intention of the driver (drivers tend to be more perceptive at low speeds). To clarify the characteristics of the launch, several test subject studies are performed. The influence factors, such as engine sound, maximal acceleration, acceleration build-up (mean jerk), and the reaction time, are taken into account. Their influence on the evaluation of launch with relation to the criteria of sportiness, comfort, and jerkiness, are examined based on the evaluation of the test subject studies. According to the results of the study, reference values for the optimization of the launch behavior are derived. The research contains a study of existing approaches for optimizing driving behavior with metaheuristics (e.g., genetic algorithms, reinforcement learning, etc.). Since the existing approaches have different drawbacks (in scope of the optimization problem) a new approach is proposed, which outperforms existing ones. The approach itself is a hybrid solution of reinforcement learning (RL) and supervised learning (SL) and is applied in a software in the loop environment, and in a test vehicle.

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Nie, Kai, Qinglei Zhou, Hong Qian, Jianmin Pang, Jinlong Xu, and Xiyan Li. "Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm." Mathematical Problems in Engineering 2021 (April 1, 2021): 1–18. http://dx.doi.org/10.1155/2021/6643604.

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Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

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Khaliqi, Atiqullah, Mohd Y. Rafii, Norida Mazlan, Mashitah Jusoh, and Yusuff Oladosu. "Genetic Analysis and Selection Criteria in Bambara Groundnut Accessions Based Yield Performance." Agronomy 11, no. 8 (August 17, 2021): 1634. http://dx.doi.org/10.3390/agronomy11081634.

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The knowledge of genetic variability and breeding techniques is crucial in crop improvement programs. This information is especially important in underutilized crops such as Bambara groundnut, which have limited breeding systems and genetic diversity information. Hence, this study evaluated the genetic variability and established the relationship between the yield and its components in Bambara groundnut based on seed weight using multivariate analysis. A field trial was conducted in a randomized complete block design with three replications on 28 lines. Data were collected on 12 agro-morphological traits, and a statistical analysis was conducted using SAS version 9.4 software, while the variance component, genotypic and phenotypic coefficient variation, heritability, and genetic advance values were estimated. A cluster analysis was performed using NT-SYS software to estimate the genetic relations among the accessions. The results showed significant variability among the accessions based on the yield and yield component characteristics. The evaluated lines were grouped into seven primary clusters based on the assessed traits using the UPGMA dendrogram. Based on the overall results, G5LR1P3, G1LR1P3, G4LR1P1, G2SR1P1 and G3SR1P4 performed the best for the yield and yield components. These improved lines are recommended for large-scale evaluation and utilization in future breeding programs to develop high-yield Bambara groundnut varieties.

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Zheng, Wei. "Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems." Scientific Programming 2022 (September 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7532076.

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In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.

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J. Tahir, M., Badri A. Bakar, M. Alam, and M. S. Mazlihum. "Optimal capacitor placement in a distribution system using ETAP software." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (August 1, 2019): 650. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp650-660.

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<p>Mostly loads are inductive in nature in content of distribution side for any power system. Due to which system faces high power losses, voltage drop and reduction in system power factor. Capacitor placement is a common method to improve these factors. To maximize the reduction of inductive load impact, optimal capacitor placement (OCP) is necessary with the objective function of system cost minimization for voltage profile enhancement, power factor improvement and power losses minimization. As OCP is a non-linear problem with equality and inequality limitations, so the stated objective depends upon he placement and sizes of the capacitor banks. Electrical transient analyzer program (ETAP) software is used for the evaluation and modelling the power systems and genetic algorithm (GA) is used as an optimization technique for the minimization of the objective function. In this paper, to show the effectiveness of the technique IEEE 4bus,33bus system and NTDC 220KV real time grid system is modelled and evaluated in terms of objective minimization i-e maximum cost saving of the power system</p>

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Saldaña, Carla L., Johan D. Cancan, Evelyn J. Salazar, Sheyla Y. Chumbimune, Jorge H. Jhoncon, and Carlos I. Arbizu. "GENETIC STRUCTURE AND DIVERSITY OF A PERUVIAN COLLECTION OF A HIGH-QUALITY WOOD TREE SPECIES, ULCUMANO (Retrophyllum rospigliosii, PODOCARPACEAE)." Chilean journal of agricultural & animal sciences 38, no. 3 (2022): 374–89. http://dx.doi.org/10.29393/chjaa38-35cebs10035.

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Ulcumano, which is native to South America, is an important conifer in Peru. Molecular studies are scarce, limiting modern breeding and appropriate conservation activities. Currently, molecular markers are widely employed to explore genetic structure and diversity parameters of plant species in a fast and precise manner. The objective of this study was to analyze the genetic diversity and population structure of ulcumano in Peru by using DNA-based molecular markers. Nine Randomly Amplified Polymorphic DNA (RAPD) markers were used, while 95 individuals of ulcumano were sampled from three departments of Peru. A total of 265 DNA fragments were manually scored, but 247 of them were kept after removing the non-polymorphic markers. Genetic distances were calculated using R software based on Provesti´s coefficient. A dendrogram was obtained using the UPGMA clustering algorithm, showing no clear clustering. The principal coordinate analysis agreed with two population structure analyses, demonstrating that ulcumano is contained within two clusters, (i) Junín + Pasco, and (ii) Cajamarca, while very few individuals are intermixed. Genetic diversity parameters were estimated considering the two groups (populations) identified by STRUCTURE software. Nei's genetic diversity estimate varied between 0.22 and 0.28, while Shannon index ranged from 3.43 to 4.16. Population divergence (Fst) between the two clusters revealed low genetic differentiation (0.064). AMOVA analysis revealed that 87.31 and 12.69% of the total genetic variation were found within populations and between individuals, respectively. To the best of our knowledge, this is the first molecular study in ulcumano in Peru, and provides valuable information for the genetic improvement and sustainable management of this conifer in the country.

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Journal articles on the topic 'Genetic improvement of software'

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