Application Of Genetic Algorithm Research Articles

Optimization of landscape garden greening design based on multi objective genetic algorithm

This paper presents a novel approach to optimize landscape garden greening design using a multi-objective genetic algorithm (MOGA)[1]. Incorporating genetic algorithms into landscape architecture offers a promising avenue for efficiently navigating the complex and multidimensional design space inherent in green infrastructure projects. Through a comprehensive bibliometric analysis of existing literature, this study synthesizes key insights into the application of genetic algorithms in landscape design and identifies gaps for further exploration[2]. Leveraging the evolutionary process of genetic algorithms, our methodology focuses on simultaneously optimizing multiple objectives such as biodiversity conservation, aesthetic appeal, water efficiency, and ecosystem services provisioning[3]. By iteratively evolving and selecting landscape configurations based on fitness criteria derived from these objectives, the MOGA enables designers to explore a diverse range of design alternatives and identify Pareto-optimal solutions that balance competing priorities. The integration of genetic algorithms into landscape design facilitates an iterative and adaptive design process, allowing for the exploration of complex trade-offs and the generation of innovative design solutions. Through a case study application, we demonstrate the effectiveness of the MOGA approach in optimizing landscape garden greening designs, showcasing its potential to enhance sustainability, resilience, and functionality in urban green spaces. This research contributes to the growing body of knowledge on computational design methods in landscape architecture and provides valuable insights for practitioners and researchers seeking to leverage genetic algorithms for optimizing green infrastructure projects.

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations.

Application of an adaptive genetic algorithm and machine learning in an English text analysis teaching system

 English text analysis is required for quantitative grammar, phrase, and word assessment to improve its usage in conversation, drafting, etc. In particular, a teaching system requires the flawless and precise use of English words, phrases, and sentences for fundamental and knowledge-based learning. Data integration and interoperability, data volume, and data variety pose difficulties for text data analytics. This article discusses a heterogeneous English teaching system text analysis solution that integrates a Genetic Algorithm (GA) and Deep Learning (DL). The Text Analytical Model (TAM) uses fused methods (FM) to handle words and their placement for sentence framing. The framed teaching sentence is analyzed lexically for its precision and meaning with conventional features. Initially, the possible word combinations using the crossover and mutation operations of the genetic process are performed. The outcome of the genetic process forecasts different possible sentence combinations for delivering the English context to students. The mutation process identifies the most precise lexical sentence that fits the subject and context. Based on precision, the DL model is trained to reduce the initial population of the GA process; this is achieved in English teaching through repetitions or drilling performed for different sentences and words. The learning converges towards precision in delivering context-based words and sentences by reducing unnecessary crossovers in the genetic process to reduce computational complexity. This feature, therefore, achieves high-precision convergence with less computation time compared to methods of the same kind. TAM-FM improves the precision convergence, forecast probability, and population refinement by 9.5%, 11.39%, and 8.81%, respectively. TAM-FM reduces the computation time and complexity by 9.67% and 8.3%, respectively.

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

Modeling and optimization of hybrid geothermal-solar energy plant using coupled artificial neural network and genetic algorithm

Avoiding solar thermal energy storage reduces efficiency in hybrid solar-geothermal energy systems, making them impractical. To address this challenge, a synergistic approach involves the integration of these resources in the construction of hybrid power plants. An experiment is conducted with five independent variables: evaporator temperature, separator pressure, inlet pressure of turbine 2, effectiveness of vapor generator 1, and desalination mass ratio. Unlike most energy systems that rely on a single optimization pattern, this study utilizes response surface methodology (RSM) to design and gather data through simulation. Additionally, an artificial neural network (ANN) is employed alongside RSM to establish mappings from independent variables to response variables, including thermal efficiency and levelized cost of product. The selection of objective functions derived from ANN is predicated on their commendable performance, denoted by an R-squared value of 1. Furthermore, a cost function is formulated with the dual aims of maximizing thermal efficiency and minimizing the levelized cost of product. This function is subsequently optimized through the application of genetic algorithms (GAs). The findings elucidate that specific parameter values—namely, a desalination mass ratio of 2.43, separator pressure of 455.77 kPa, effectiveness of vapor generator 1 of 0.82, inlet pressure of turbine 2 of 12000 kPa, and evaporator temperature of −11.51 ℃—conducive to the optimal condition are identified, yielding a thermal efficiency of 30.47% and a levelized cost of product of 13.04 $/GJ. This endeavor is anticipated to furnish an algorithmic framework not only for modeling hybrid plants but also for optimizing electrical power generation processes.

Application of Genetic Algorithms for Medical Diagnosis of Diabetes Mellitus

The system of glucose-insulin control and associated problems in diabetes mellitus were studied by mathematical modeling. It is a helpful theoretical tool for understanding the basic concepts of numerous distinct medical and biological functions. It delves into the various risk factors contributing to the onset of diabetes, such as sedentary lifestyle, obesity, family history, viruses, and increasing age. The study emphasizes the importance of mathematical models in understanding the dynamic characteristics of biological systems. The study emphasizes the increasing prevalence of diabetes, especially in India, where urbanization and lifestyle changes contribute to the rising incidence. The present investigation describes the use of John Holland's evolutionary computing approach and the Genetic Algorithm (GA) in diabetes mellitus. The Genetic Algorithm is applied to address issues related to diabetes, offering a generic solution and utilizing MATLAB's Genetic Algorithm tool. The Mathematical Model provides differential equations representing glucose and insulin concentrations in the blood. The results represent testing outcomes for normal, prediabetic, and diabetic individuals, optimized with Genetic Algorithm showcased through fitness value plots. The conclusion highlights the effectiveness of Genetic Algorithm as an optimization tool in predicting optimal samples for diabetes diagnosis. The paper encourages the use of heuristic algorithms, such as Genetic Algorithms, to address complex challenges in the field of diabetes research. Future scope includes further exploration of biomathematics and Genetic Algorithm applications for enhanced understanding and management of diabetes mellitus. It is critical for people with diabetes to consistently check their blood glucose levels and follow their treatment plan.

Application of artificial neural networks and genetic algorithm in optimization of concrete shear wall design

Application of artificial neural networks and genetic algorithm in optimization of concrete shear wall design

Application of ARIMA agorithm and genetic algorithm based on MATLAB language in finance

The price changes of bitcoin and gold are frequent, as observed in previous studies, and market participants can base their daily investment plans on historical data. Market traders can maximise their investment returns by buying low and selling high on volatile assets based on the day's market conditions, as markets fluctuate every day. Gold and bitcoin are two of the most common volatile investments. In this project, we are trying create an algorithm of high degree of integration which can provide advice to traders before they made their decisions. We used ARIMA algorithm based on the Genetic Algorithm to predict the data and let the algorithm to make based on the predicted data. The complete code could be seen in appendix. The codes were mostly based on MATLAB language, for which it integrated the features of simple and easy to get started; dispatchable sensitivity; adaptable and reliable. Which allows the traders to use simple operations to get reliable and visualizable results. Which can let the traders to have the opportunity to get suggestions on when they should trade.

Improving voltage collapse point under transmission line outage by optimal placement and sizing of SVC using genetic algorithm

In many power systems, voltage instability can increase the risk of voltage collapse and, as a result, turn the power system toward a blackout. Therefore, increasing the voltage collapse point is required. A transmission line outage is an emergency condition in power systems that can lead to voltage instability and voltage collapse. Thus, it is expected to employ shunt-connected flexible AC transmission systems (FACTS) such as the static var compensator (SVC) to increase the voltage collapse point when lines outage. This paper presents the genetic algorithm (GA) application to optimal placement and sizing of an SVC for increasing voltage collapse points following lines outage. The continuation power flow (CPF) technique has been used to determine the maximum loading point (MLP) corresponding to the point of voltage collapse. Also, to reduce the number of scenarios when line outages occur, a list in ascending order is established based on the line outage priority (LOP). The IEEE 14-bus test system is chosen to carry out simulations, and an SVC will be installed in the system based on the GA results. Simulation results confirm the effectiveness of an SVC for improving voltage stability as well as increasing voltage profile.

Application of genetic algorithm to enhance the predictive stability of BP-ANN constitutive model for GH4169 superalloy

Application of genetic algorithm to enhance the predictive stability of BP-ANN constitutive model for GH4169 superalloy

Fuzzy cognitive maps for municipal governance improvement.

This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.

Application of Genetic Algorithms for Periodicity Recognition and Finite Sequences Sorting

Unpredictable strings are sequences of data with complex and erratic behavior, which makes them an object of interest in various scientific fields. Unpredictable strings related to chaos theory was investigated using a genetic algorithm. This paper presents a new genetic algorithm for converting large binary sequences into their periodic form. The MakePeriod method is also presented, which is aimed at optimizing the search for such periodic sequences, which significantly reduces the number of generations to achieve the result of the problem under consideration. The analysis of the deviation of a nonperiodic sequence from its considered periodic transformation was carried out, and methods of crossover and mutation were investigated. The proposed algorithm and its associated conclusions can be applied to processing large sequences and different values of the period, and also emphasize the importance of choosing the right methods of crossover and mutation when applying genetic algorithms to this task.

Application of Genetic Algorithm in Optimization Simulation of Industrial Waste Land Reuse

In order to better understand the application of optimization simulation for industrial waste land reuse, the author proposes an application study based on nonlinear genetic algorithm in the optimization simulation of industrial waste land reuse. The author takes the landscape renovation and reuse of industrial waste sites as the research object, and through research on the current situation of landscape renovation and reuse of industrial waste sites both domestically and internationally, as well as on-site inspections, attempts to use landscape design techniques to deal with this once glorious but destructive industrial landscape that has already declined. Secondly, a genetic algorithm for enhancing the timeliness of industrial waste land reuse is proposed, which is based on random walks, combine users' long-term and short-term preferences to calculate the most suitable Top-N industrial waste land reuse optimization model for the current period. Finally, the two algorithms proposed by the author were experimentally validated on the dataset. In the CiteU Like dataset, the best performance was achieved at a=0.4, while in the JD dataset, the best performance was achieved at a=0.6. When k=6, the hit rate significantly decreases by about 50%. The URT-R genetic algorithm exhibits a high recommendation hit rate in recommendations targeting timeliness. The author analyzed the different characteristics of industrial waste reuse in scenic areas and optimized their essence and transformation methods, further improving the transformation and renewal methods of industrial waste land in the process of urban development in China. I hope to provide useful references for future research on related topics and practices.

Real-time sensor networks based on genetic algorithms application in the analysis of innovative data in cultural industry management

Against the backdrop of sustained economic development and steady improvement of China's overall national strength, the cultural industry is in a good stage of development, but it also faces problems such as data complexity and difficulty in organizing. Based on this, we propose a management innovation data analysis system based on genetic algorithm and real-time technology of the Internet of Things, and apply this method to cultural industry management to analyze relevant management data. It was found that based on the improved genetic algorithm, it can achieve better optimization results with less computational complexity. Meanwhile, genetic algorithms based on penalty functions adopt a non-uniform mutation operator, which can accelerate convergence. Compared with existing genetic algorithms, the new genetic algorithm has lower computational complexity and lower system average overhead, greatly improving work efficiency and making its work cost lower. On this basis, combined with theoretical and empirical research results, provide theoretical and practical support for the sustainable development of the industry.

A Review of Genetic Algorithm: Operations and Applications

This article presents a review of the Genetic Algorithm (GA), a prominent optimization technique inspired by natural selection and genetics. In the context of rapidly evolving computational methodologies, GA have gained considerable attention for their efficacy in solving complex optimization problems across various domains. The background highlights the growing significance of optimization techniques in addressing real-world challenges. However, the inherent complexity and diversity of problems necessitate versatile approaches like GA. The problem statement underscores the need to explore the underlying operations and applications of GA to provide a nuanced understanding of their capabilities and limitations. The objectives of this review encompass delving into the fundamental genetic operators, such as selection, crossover, and mutation, while examining their role in maintaining diversity and converging toward optimal solutions. Methodology-wise, a systematic analysis of existing literature is undertaken to distil key insights and trends in GA applications. The main findings show the adaptability of GA in tackling problems spanning engineering, economics, bioinformatics, and beyond. By facilitating the discovery of optimal or near-optimal solutions within large solution spaces, GA proves its mettle in scenarios where traditional methods fall short. The conclusion underscores the enduring relevance of GA in the optimization landscape, emphasizing their potential to remain a pivotal tool for addressing intricate real-world challenges, provided their parameters are fine-tuned judiciously to balance exploration and exploitation.

A comprehensive automatic labeling and repair strategy for cracks and peeling conditions of literary murals in ancient buildings

To protect the historical and cultural heritage, the application of self-organizing mapping networks and genetic algorithms in the restoration of ancient architectural murals is studied. The results show that the average repair time for different types of mural paintings is less than 60 seconds, and the shortest repair time is only 17.81 seconds. The evaluation effect of the research model is good, and the comprehensive efficiency evaluation of the mural restoration work is improved by about 40.42%. The repair system has excellent performance, and the algorithm has high feasibility and effectiveness. The impact of restoring murals is substantial, and the extent of restoration is highly consequential for the restoration of ancient architectural murals.

Utilization of a Genetic Algorithm to Identify Optimal Geometric Shapes for a Seismic Protective Barrier

The utilization of seismic barriers for protection against the hazardous impact of natural or technogenic waves is an extremely promising emerging technology to secure buildings, structures and entire areas against earthquake-generated seismic waves, high-speed-transport-induced vibrations, etc. The current research is targeted at studying the effect of seismic-barrier shape on the reduction of seismic-wave magnitudes within the protected region. The analytical solution of Lamb’s problem was used to verify the adopted numerical approach. It was demonstrated that the addition of complementary geometric features to a simple barrier shape provides the possibility of significantly increasing the resulting seismic protection. A simple genetic algorithm was employed to evaluate the nontrivial but extremely effective geometry of the seismic barrier. The developed approach can be used in various problems requiring optimization of non-parameterizable geometric shapes. The applicability of genetic algorithms and other generative algorithms to discover optimal (or close to optimal) geometric configurations for the essentially multiscale problems of the interaction of mechanical waves with inclusions is discussed.

Retracted: Research on the Application of Genetic Algorithm in Physical Education

Retracted: Research on the Application of Genetic Algorithm in Physical Education

Optimization of GFRP-concrete-steel composite column based on genetic algorithm - artificial neural network

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS.