Integration Of Genetic Algorithm Research Articles

Genetic Algorithms and Swarm Intelligence in Computational Mathematics for Predictive Modelling

The integration of genetic algorithms and swarm intelligence into computational mathematics has significantly advanced the field of predictive modeling. Genetic algorithms use the ideas behind natural selection and genetics to find the best answers to difficult problems over and over again. These algorithms are great at working with big, multidimensional search areas. This makes them perfect for many predictive modeling tasks, such as genomics, financial predictions, and climate modeling. The idea behind swarm intelligence comes from the way social animals like ants, bees, and birds act as a group. It uses autonomous, self-organized systems to improve predictive models. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two examples of techniques that use simple agents that follow basic rules to solve difficult optimization problems. In order to improve and confirm models, these programs often use computational mathematics methods like linear algebra, statistical analysis, and differential equations. When you combine genetic algorithms, swarm intelligence, and these mathematical methods, you get a strong framework for dealing with the computational problems that come up in predictive modeling. This combined method makes it easier to explore and use the search area effectively, raises the rate of convergence, and improves the quality of the solutions. This combination can be used in many areas, such as engineering to improve design parameters, economics to guess market trends, and healthcare to make diagnoses more accurate. In addition, the fact that these methods can be changed to include domain-specific knowledge adds to their usefulness and flexibility. Genetic algorithms and swarm intelligence are continuing to improve with the help of more complex mathematical and computational methods. This will lead to even better tools for making predictions, which will encourage new ideas in many scientific and economic fields.

Integration of Genetic Algorithm and Hedge Algebras in controlling mechanical machining robots

Integration of Genetic Algorithm and Hedge Algebras in controlling mechanical machining robots

Unveiling optimal half-cell potentials in RCC slabs through cutting-edge ANFIS, ANN and genetic algorithm integration

PurposeThe purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.Design/methodology/approachIn this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.FindingsIn this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.Originality/valueBased on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.

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.

Optimizing Design Parameters with a Hybrid Approach: TensorFlow and Genetic Algorithm Integration

Optimizing Design Parameters with a Hybrid Approach: TensorFlow and Genetic Algorithm Integration

Optimization model for selecting optimal prefabricated column design considering environmental impacts and costs using genetic algorithm

Prefabricated columns are commonly used in construction projects due to their advantages, such as high quality, fast construction, environmental friendliness, and cost-effectiveness. Toward achieving the minimization of environmental impacts and costs through the prefabricated system at the same time, the optimal design should be conducted during the pre-construction phases. Therefore, this research aims to develop an optimization model that uses a genetic algorithm to select optimal prefabricated column designs considering both environmental impacts and costs. This study was conducted in four steps: (i) Data collection, (ii) Calculation of environmental impacts, (iii) Economic assessment, and (iv) Optimization using a genetic algorithm. The results showed that the average environmental impacts for global warming potential (GWP), ozone layer depletion potential (ODP), acidification potential (AP), eutrophication potential (EP), photochemical ozone creation potential (POCP), and abiotic depletion potential (ADP), were 8382.10 kg-CO2, 1.76 × 10−4 kg-CFC-11, 11.03 kg-SO2, 1.89 kg-PO43-, 6.94 kg-C2H4, and 47.60 kg-sb, respectively, with an integrated value of $177.14. The average total cost of the prefabricated column was $4591.17. The overall environmental impacts and costs of the prefabricated columns were $26,020.37 and $680,477.36, respectively, whereas the optimal columns were $19,219.56 and $492,708.32, representing reductions of 26.14% and 27.59%, respectively. The implications of this research are of great importance for sustainable construction practices, as it provides a practical framework for selecting optimal prefabricated column designs that simultaneously minimize environmental impacts and costs. The integration of genetic algorithms within the optimization model enables the identification of environmentally friendly and cost-effective solutions, contributing to the overall reduction of environmental impacts associated with construction activities.

Integration of genetic algorithm with artificial neural network for stock market forecasting

Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.

Inverse identification of pollution source release information for surface river chemical spills using a hybrid optimization model

To evaluate the liability of the spilled contaminant and to design comprehensive emergency response schemes, it is essential to estimate the contaminant source characteristic and identify where, when and how much the spilled contaminant is injected into a surface river. In this study, an effective pollution source inverse method is developed to reconstruct the release history of the injection location, time, and quantity, and provide appropriate emergency response schemes for dealing with surface river environmental pollution. The pollution source inverse method IGSAA is developed by an integration of genetic algorithm (IGA) and simulated annealing algorithm (SAA) in order to guarantee both the global searching ability and convergence speed. The pollution source inverse method IGSAA is then applied to a hypothetical study, comparing with the traditional GA-based and SAA-based methods, to verify the accuracy and efficiency of the contaminant source inverse, and to a trace study of Truckee River in west America to identify the contaminant source release history and characteristic under different scenarios. The pollution source inversion results can help decision-makers (DMs) to identify the contaminant source characteristics of a chemical spill, and carry out emergency disposal scheme for an emergency rescue in a quick response, and enhance the supervision and management ability for a real surface river system.

Integration of genetic algorithms and neural networks for the formation of the classifier of the hierarchical Choquet integral

The Choquet integral model is mainly applied to describe non-additive multi-criteria decision-making (MCDM) problems. This paper considers the Choquet integral as a classifier which deals with complicated high-dimensional data. Although previously conducted studies have investigated the problem of classification using the Choquet integral and provided corresponding models, these models usually need to estimate a large number of fuzzy measure coefficients, which are not suitable when considering real situations. Sugeno et al. proposed the hierarchical Choquet integral (HCI) model to overcome this problem. However, the HCI model requires partition information of the criteria, which often cannot be obtained practically. This paper proposes two HCI models—shallow and deep models—by employing genetic algorithms (GAs) and neural networks (NNs) to automatically construct the structure of the HCI. The results of numerical experiments show that the proposed model outperforms the existing Naïve Bayes, decision tree, and NN models.

An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks.

Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.

Genetic Algorithm: Review and Application

Genetic algorithms are considered as a search process used in computing to find exact or a approximate solution for optimization and search problems. There are also termed as global search heuristics. These techniques are inspired by evolutionary biology such as inheritance mutation, selection and cross over. These algorithms provide a technique for program to automatically improve their parameters. This paper is an introduction of genetic algorithm approach including various applications and described the integration of genetic algorithm with object oriented programming approaches. The Genetic algorithm is an adaptive heuristic search method based on population genetics. Genetic algorithm were introduced by John Holland in the early 1970s (1).Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. Genetic algorithm is started with a set of solutions called population. A solution is represented by a chromosome. The population size is preserved throughout each generation. At each generation, fitness of each chromosome is evaluated, and then chromosomes for the next generation are probabilistically selected according to their fitness values. Some of the selected chromosomes randomly mate and produce offspring. When producing offspring, crossover and mutation randomly occurs. Because chromosomes with high fitness values have high probability of being selected, chromosomes of the new generation may have higher average fitness value than those of the old generation. The process of evolution is repeated until the end condition is satisfied. The solutions in genetic algorithms are called chromosomes or strings (2). In most cases, chromosomes are represented by lists or strings. Thus, many operations in genetic algorithm are operations on lists or strings. The very high level languages like Python or Perl are more productive in list processing or string processing than C/C++/Java. In bioinformatics, Python or Perl is widely used. A genetic algorithm is a search technique used in computing to find exact or approximate solutions to

Optimization of Metamaterials-Based Wi-Fi Antenna Using Genetic Algorithm

This paper describes a microstrip patch antenna embedded with SSRR structures in the ground plane for ISM applications. The antenna characteristics are suitably enhanced by incorporating the metamaterials, as these are directly etched in the ground, without disturbing its radiation patch. SSRR directly interacts with electric fields making it persuade out the left-handed behavior of metamaterials, which is usually stated by series capacitance and shunt inductance. Integration of genetic algorithm to the proposed metamaterials antenna yields an enhanced gain of 7.696 dBi with a size reduction of 16.89%.

Transfer Coordination-Based Train Organization for Small-Size Metro Networks

This paper addresses the train organization problem of a small-size metro network during the peak hour, with a special consideration of transfer coordination. The problem is formulated as a multi-objective programming (MOP) model, where the economic cost, capacity utilization, and transfer coordination are considered together based on the routing selection analysis. Train marshaling number and headway on different routes are key decision variables in the model. The economic cost consists of generalized trip cost, operation cost, and external benefits, and the capacity utilization describes the remaining section capacity expressed by a difference quadratic sum. The proposed model highlights the transfer coordination, which targets at minimizing the number of left-behind passengers on platforms, considering the time-varying arrival rate and the remaining train capacity. Based on the sequencing method, an integration of genetic algorithm and ant colony optimization is devised to solve the MOP model. Finally, a real-world case study of Xi’an metro network has been conducted. Results show that the number of left-behind passengers in the network decreased 55.3$$\%$$, the total remaining capacity decreased 15.3$$\%$$ and the number of trains decreased 8.5$$\%$$, while the total time cost increased 1.6$$\%$$. To further check the passenger distribution density on the platform, a simulation of Beidajie transfer station has been elaborately designed via Viswalk 7.0. Both theoretical results and simulation data have validated the feasibility and reliability of presented method.

New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.

A polychromatic sets theory based algorithm for the input/output scheduling problem in AS/RSs

This paper proposes a hybrid polychromatic sets theory based genetic algorithm (GA) for the input/output scheduling problem of automated storage and retrieval systems (AS/RSs). In order to overcome the drawbacks of precocious phenomena and lack of stability of the GA, a novel hybrid model through integration of genetic algorithm (GA) and polychromatic sets theory (PS) is proposed. During the solution process, polychromatic sets contour matric is used to assign input/output goods location reasonably to improve the quality of initial population. During the iterative process, simulate anneal algorithm (SA) is invoked to jump out of the local optimum to obtain a satisfactory solution. The experiment results showed that the hybrid PS-SA-GA genetic algorithm has obvious advantages in solving the input/output scheduling problems: the introduction of contour matrix constraint module can effectively improve the quality of initial population; the simulated annealing algorithm is invoked to reduce the possibility of premature convergence, and the global optimal solution can be obtained efficiently.

Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN)

In this study, we used both methods of ANN and GAINN for predicting the fibrous filter’s efficiency. In this regard, we collected the experimental penetration data for particles in the range of 10.7–191.1 nm. Experimental data were collected with different constant flow rates and from one type of N95 filtering facepiece respirator. A satisfactory number of data from experimental setup were exploited to build up a database. These methods are according to the back-propagation algorithm to map two components, namely, particle diameter and constant air flow rates into the corresponding penetration. The developed ANN and GAINN methods were capable of predicting precise values of penetration from experimental data. Also by comparing the results of these two methods, it is understandable that ANN method can predict the penetration data from examples of the experimental setup more efficiently than GAINN within an acceptable computational time.

Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems

Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.

Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth

There are two main issues of concern for land change scientists to consider. First, selecting appropriate and independent land cover change (LCC) drivers is a substantial challenge because these drivers usually correlate with each other. For this reason, we used a well-known machine learning tool called genetic algorithm (GA) to select the optimum LCC drivers. In addition, using the best or most appropriate LCC model is critical since some of them are limited to a specific function, to discover non-linear patterns within land use data. In this study, a support vector regression (SVR) was implemented to model LCC as SVRs use various linear and non-linear kernels to better identify non-linear patterns within land use data. With such an approach, choosing the appropriate kernels to model LCC is critical because SVR kernels have a direct impact on the accuracy of the model. Therefore, various linear and non-linear kernels, including radial basis function (RBF), sigmoid (SIG), polynomial (PL) and linear (LN) kernels, were used across two phases: 1) in combination with GA, and 2) without GA present. The simulated maps resulting from each combination were evaluated using a recently modified version of the receiver operating characteristics (ROC) tool called the total operating characteristic (TOC) tool. The proposed approach was applied to simulate urban growth in Rasht County, which is located in the north of Iran. As a result, an SVR-GA-RBF model achieved the highest area under curve (AUC) value at 94% while the lowest AUC was achieved when using the SVR-LN model at 71%. The results show that the synergy between GA and SVR can effectively optimize the variables selection process used when developing an LCC model, and can enhance the predictive accuracy of SVR.

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm