Variable Length Genetic Algorithm Research Articles

Multipath traveling purchaser problem with time-dependent market structure using quantum-inspired variable length genetic algorithm

In developing countries, rural and semi-urban markets remain open for a particular period of time in a day, and items’ availabilities and prices vary within the opening hours of a market. Nowadays, the Internet of Things (IoT) and Type-2 fuzzy logic (T2FL) systems are used to make real-time decisions. With the logistical and infrastructural development around the world, there are several alternate routes for travel between different markets and cities. Taking the above realities into consideration, here, we develop and analyze IoT-enabled and T2FL system-based multipath traveling purchaser problems with time-dependent market structure (IoT-T2FL-MPTPPswTDMS). In this study, information regarding weather, road surface, and congestion concerning different paths obtained by the IoT are used as inputs in T2FL, and path-wise, the vehicle’s average velocity is obtained using the fuzzy rules. Item availability and price in a particular market change with time. The optimization problem is, for different journey starting times, to select the appropriate markets for the purchase and the corresponding optimum routing path for minimum cost (time) against time (cost) constraint. A multi parent, varied-offspring, variable length quantum-inspired genetic algorithm (MPVOVLQiGA) with quantum selection and mutation and multi parent, varied-offspring crossover is developed to solve the proposed problems. For children, mimicking the real-life parental system, In vitro fertilization (IVF) and adopted child (if IVF failed) are considered alongwith the usual comparison crossover. The algorithm’s superiority is established through a statistical test. A real-life model is numerically illustrated. The dependence on the minimum cost and time on the journey’s commencement time is demonstrated.

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Abstract: Convolutional Neural Networks (CNNs) have gained widespread recognition in the field of computer vision, specifically for handwritten digit recognition. Despite their remarkable accuracy, CNNs entail significant computational training demands and are susceptible to local optima, necessitating innovative optimization strategies. This research introduces a novel approach to hyperparameter tuning for CNN models tailored to the recognition of English-Devanagari handwritten digits. This method combines a Hybrid Evolutionary Algorithm (HEA) with a Variable Length Genetic Algorithm (VLGA) and leverages a comprehensive dataset encompassing both English and Devanagari handwritten digits. The strategy extends the conventional paradigm by integrating a variable-length GA, facilitating systematic and adaptive tuning of critical CNN hyperparameters, including optimizer selection, learning rate, global hyperparameters, kernel size, filter count, activation functions, layer count, and pooling mechanisms. Extensive experimentation across benchmark datasets underscores the superior performance of the proposed approach when compared to traditional optimization methods. Seven key hyperparameters are the focal point of optimization efforts: learning rate, optimizer, kernel size, filter count, activation function, layer count, and pooling strategy. The results highlight the significant performance boost achieved by CNNs assisted by genetic algorithms, underscoring the effectiveness of evolutionary approaches in CNN training. Notably, experiments reveal that a population size of 27 yields optimal fitness values and average fitness scores. In the culmination of this research, the HEA-VLGA model achieves an impressive accuracy rate of approximately 99.38%. These findings unequivocally affirm the efficacy of incorporating evolutionary techniques as a potent avenue for enhancing CNN training processes.

Multi-vehicle clustered traveling purchaser problem using a variable-length genetic algorithm

In this paper, we propose a multi-vehicle clustered traveling purchaser problem (MVCluTPP). Here, two types of procurement planning are proposed. In the first setup, the purchaser visits the markets clusterwise and, after satisfying the demand, returns to the depot with the purchased products, which are carried on the same path using a different vehicle. The other set up, in which products are purchased clusterwise and transported directly from the center of the cluster to the depot, but with the mandate that the purchaser visits the markets clusterwise. One of the multi-pronged aims of the model is to select the clusters and identify which markets to visit, determine the amount of procurement available in each market in a cluster, and develop an optimal routing plan in such a way that the overall system cost is minimized. The clusters are generated using k-means algorithm, and a variable-length chromosome genetic algorithm (VLC-GA) is proposed to optimize the cluster paths and to use a local heuristic to link the clusters to minimize the system cost. Furthermore, the superiority of the VLC-GA has been established through standard TPP and TSP instances, compared with exact methods, and some statistical tests are presented.

Joint on-demand data gathering and recharging by multiple mobile vehicles in delay sensitive WRSN using variable length GA

Numerous state-of-the-art works on joint data gathering and charging (DGAC) believe that the mobile vehicle (MV) has sufficient energy to charge the sensors and collect data from them by travelling across the path. The existing works also considered that the sensors always get full charge from MV which produces negligible dead period and minimum data gathering delay in Wireless Rechargeable Sensor Network (WRSN). However, the aforementioned considerations are not always practical in large-scale WRSN. In this article, we introduce an efficient on-demand partial DGAC scheme using battery limited multiple MVs. The proposed scheme executes the joint DGAC process such that the total dead periods of the sensors and the overall data gathering delay is reduced. We use multi objective-based genetic algorithm (GA) to optimize the entire DGAC process. The simulation is performed to demonstrate the usefulness and competitiveness of the proposed scheme. We also verify the statistical significance of the simulation results using ANalysis Of VAriance (ANOVA). The comparative analysis reveals that the proposed work improves the DGAC efficiency by reducing the dead period of sensors, data gathering delay, and energy consumption of MVs in comparison with the existing works.

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.

Optimization of Base Station Placement in 4G LTE Broadband Networks Using Adaptive Variable Length Genetic Algorithm

Optimization of Base Station Placement in 4G LTE Broadband Networks Using Adaptive Variable Length Genetic Algorithm

Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine

One of the challenges that data-based gas path component monitoring systems are facing, is the poor performance in those operational conditions that are not considered in the training process. An approach could be retraining the model repeatedly with a large number of previous and fresh data from the scratch, which would require a lot of time and storage memory. To address this problem, a component fault diagnostic system based on the bank of online sequential extreme learning machines (OSELMs) are applied that can incrementally update with any number of new training samples. This system is adapted for a two-shaft industrial gas turbine that is used for power generation. Besides, an optimal set of measurements are selected as the input of the system using a hybrid approach based on the variable length genetic algorithm and extreme learning machine (VLGA-ELM). The performance of OSELM based diagnostic system is compared with several batch learning algorithms by analyzing the confusion matrix of all systems. The results show that the OSELM based approach reflects a promising compromise between the overall accuracy, false positive rate, F1-score and false negative rate, while having the least training time compared with the other systems.

Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network

AbstractAtrial fibrillation (AF) is the most prevalent cardiac arrhythmia and it is considered as one of the most important risk factor for death, stroke, hospitalization, and heart failure. It is possible to detect AF by analyzing electrocardiogram (ECG) of patients. To work on clean signals and reduce errors resulted from noise, we have used Butterworth filter. The short‐term Fourier transform was used to analyze ECG segments to obtain ECG spectrogram images. Convolutional neural network (CNN) models have been proposed for improving automatic detection of AF. The number of convolutional layers varies in different CNN models, and as the model become deeper, more hyper parameters are added. So in this article, variable length genetic algorithm was used in order to optimize hyper parameters of CNN. The results of experiments that performed on the MIT‐BIH AF database showed that the proposed method achieved 100%, 98.90%, and 99.95% for the sensitivity, specificity, and accuracy, respectively, so the proposed method outperforms the deep CNNs. Hence, the proposed method is an accurate and efficient method for detection of AF.

Diversity metrics for direct-coded variable-length chromosome shortest path problem evolutionary algorithms

One of the major concerns in Evolutionary Algorithms is the premature convergence caused by what is known as the Exploration and Exploitation Balance problem. To maintain this balance, population diversity should be maintained during the initialization/optimization process. Maintaining diversity can be done through different strategies, but they commonly answer one question: when to introduce more diversity to the population? To answer this question there should be diversity metrics upon which a decision can be made to add diversity; consequently, add/reduce exploration/exploitation. There are as many diversity metrics as many problems and representations. That is, diversity metrics are very problem-specific. This work provides diversity metrics for the variable-length chromosome Genetic Algorithm for Shortest Path. The suggested metrics consider the varying lengths of the chromosomes, problem representation, and the search space. To measure chromosome-length diversity, a novel chromosome-length-based metric has been proposed. By exploiting the fact that the possible genes that can form any chromosome are well known in this specific direct-encoded population, a new simple metric that measures the representation of genes in the initial population is proposed and experimentally investigated. The presented metrics put through an extensive simulation and comparatively studied. Relationship between the proposed metrics has been quantified using Principal Component Analysis under varying network/population sizes.

Clustering of large scale QoS time series data in federated clouds using improved variable Chromosome Length Genetic Algorithm (CQGA)

Service monitoring in federated clouds generates large scale QoS time series data with various unknown, frequent and abnormal patterns. This could be associated with inaccurate resource provisioning and avoid violations through predictive and preventive actions. A sufficient intelligence in the form of expert system for decision support is needed in such situations. Therefore, the main challenge here is to efficiently discover unknown frequent and abnormal patterns from QoS time series data of federated clouds. On the other hand, QoS time series data in federated clouds is unlabeled and consists of frequent and abnormal structures. Studies showed that clustering is the most common and efficient method to discover interesting patterns and structures from unlabeled data. But, clustering is normally associated with time overhead that should be optimized as well as accuracy issues mainly in connection with convergence and finding an optimum number of clusters. This work proposes a new genetic based clustering algorithm that shows better accuracy and speed in comparison to state-of-the-art methods. Furthermore, the proposed algorithm can find the optimum number of clusters concurrently with the clustering itself. Achieved accuracy and convergence of the proposed method in the experimental results assure its use in expert systems, mainly for resource provisioning and further autonomous decision making situations in federated clouds. In addition to the scientific impact of this paper, the proposed method can be used by federated cloud service providers in practice.

Web services recommendation leveraging semantic similarity computing

With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users' requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.

Web Services Recommendation Leveraging Semantic Similarity Computing

With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users’ requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.

Recommending diverse friends in signed social networks based on adaptive soft consensus paradigm using variable length genetic algorithm

Despite the strategic role played by individuals, who act as intermediaries between distinct groups of people, the problem of recommending diverse friends in signed social networks (SSNs) still remains largely unexplored. Our model integrates homophily and diversity to develop an adaptive consensus based framework, which involves fuzzy group decision making analysis by leveraging on the signed social links and underlying users’ preferences, to offer lists of connections which are diverse as well as relevant. Our contributions are three-fold. First, we modeled the fuzzy binary adjacency relations between users, thereafter referred as decision makers (DMs), exploiting users’ preferences conferred on a set of items, and then higher order fuzzy m-ary adjacency relations are constructed to represent the grade of agreement between a set of m DMs. Further, in order to evaluate the relevance of each decision maker involved in the decision making process, we introduce a novel diversity measure based on the knowledge of socio-psychological theories and the information contained in social and interest links. Next, by employing variable-length genetic algorithm, an idea of adaptive consensus is explored to evolve groups of experts which are highly consensual as well as influential in the social network. Finally, on the basis of opinions gleaned from the members of these groups, sign of unknown links are predicted, thereby generating a top-N recommendations list of diverse friends. Extensive experimental study conducted on Epinions dataset illustrates that our proposed scheme outperforms the traditional graph-based methods.

Learning path recommendation based on modified variable length genetic algorithm

With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing is considered as an important concern for developing more efficient personalized e-learning systems. A more effective personalized e-learning recommender system should recommend a sequence of learning materials called learning path, in an appropriate order with a starting and ending point, rather than a sequence of unordered learning materials. Further the recommended sequence should also match the learner preferences for enhancing their learning capabilities. Moreover, the length of recommended sequence cannot be fixed for each learner because these learners differ from one another in their preferences such as knowledge levels, learning styles, emotions, etc. In this paper, we present an effective learning path recommendation system (LPRS) for e-learners through a variable length genetic algorithm (VLGA) by considering learners’ learning styles and knowledge levels. Experimental results are presented to demonstrate the effectiveness of the proposed LPRS in e-learning environment.

Agent-based modeling and evolutionary computation for disseminating public advisories about hazardous material emergencies

In the event of a large-scale disaster, an important aspect of humanitarian logistics is the distribution of information or warnings to the affected population. This research develops the problem formulation and solution approach for a specific routing for relief problem, in which warnings should be disseminated to an affected community, using public announcement systems mounted on emergency vehicles. The problem statement is formulated to maximize the number of individuals of a community who are protected. An evolutionary algorithm framework is developed by coupling an agent-based model with a variable-length genetic algorithm to route emergency vehicles. The dynamics of interactions among consumers, emergency vehicles, and the spatiotemporal trajectory of the hazard are simulated using an agent-based modeling approach, and a variable-length genetic algorithm approach selects routes to warn a maximum number of consumers before they are affected by the emergency. The example that is explored in this research is contamination of a water distribution network. A fleet of emergency vehicles is equipped with public address systems and is deployed to warn consumers to stop using contaminated water. The framework is demonstrated for an illustrative virtual city, Mesopolis. The results of the evolutionary algorithm framework are compared with two conventional routing optimization approaches, including a covering tour problem approach and a manual routing approach, for four contamination scenarios. The evolutionary algorithm can be applied to route emergency service vehicles to broadcast information for other emergencies, such as flash flooding, hazardous materials incidents, and severe weather.

A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry

Due to the error-prone nature of garment manufacturing operations, it is challenging to guarantee the quality of garments. Previous research has been done to apply fuzzy association rule mining to determine process settings for improving the garment quality. The relationship between process parameters and the finished quality is represented in terms of rules. This paper enhances the application by encoding the rules into variable-length chromosomes for optimization with the use of a novel genetic algorithm (GA), namely the slippery genetic algorithm (sGA). Inspired by the biological slippage phenomenon in DNA replication, sGA allows changes to the chromosome lengths by insertion and deletion. During rule optimization, different parameters can be inserted to or removed from a rule, increasing the diversity of the solutions. In this paper, a slippery genetic algorithm-based process mining system (sGAPMS) is developed to optimize fuzzy rules with the aim of facilitating a comprehensive quality assurance scheme in the garment industry. The significance of this paper includes the development of a novel variable-length GA mechanism and the hybridization of fuzzy association rule mining and variable-length GAs. Though the capability of conventional GA in rule optimization has been proven, the diversity in the population is inherently limited by the fixed chromosome length. Motivated by this phenomenon, the sGA suggested in this paper allows various parameters to be considered in a rule, improving the diversity of the solutions. A case study is conducted in a garment manufacturing company to evaluate the sGAPMS. The results illustrate that better quality assurance can be achieved after rule optimization.

Solving the container pre-marshalling problem using variable length genetic algorithms

In container terminals, the yard area consists of a set of blocks, which consists of a set of bays. Each bay consists of a set of stacks, which consists of a set of tiers. In the container pre-marshalling problem, an initial layout of a bay is converted to a final desired layout. The final layout follows the given loading schedule of this bay. This has a direct impact on the most important container terminal performance measure: the vessel loading time. The deviation between the current layout and the desired layout is expressed by the value of the mis-overlays. The objective of the pre-marshalling problem is to eliminate the mis-overlays with the minimum number of container movements. In this article, a variable chromosome length genetic algorithm was applied to solve the problem. The results of the new solution approach were compared against benchmark instances and the results were remarkably better.

A genetic algorithm based decision support system for the multi-objective node placement problem in next wireless generation network

The node placement problem involves positioning and configuring infrastructure for wireless networks. Applied to next generation networks, it establishes a new wireless architecture able to integrate heterogeneous components that can collaborate and exchange data. Furthermore, the heterogeneity of wireless networks makes the problem more intractable. This paper presents a novel multi-objective node placement problem that optimizes concurrently four objectives: maximizing communication coverage, minimizing the active structures’ costs, maximizing of the total capacity bandwidth and minimizing the noise level in the network. Known to be NP-hard, the problem can be approached by applying heuristics mainly for large problem instances. As the number of nodes to place is not determined beforehand; we propose to apply a multi-objective variable-length genetic algorithm (VLGA) that simultaneously searches for the optimal number, positions and nature of heterogeneous nodes and communication devices. The performance of the VLGA is highlighted through the implementation of a decision support system (DSS) applied to the surveillance maritime problem using real data instances. We compare the ability of the proposed algorithm with an existing multi-objective model from the literature in order to validate its effectiveness in dealing with heterogeneous components. The results show that the proposed model well fits the network architecture constraints with a better balance between the objectives applied to the surveillance problem.

Automatic generation of basis test paths using variable length genetic algorithm

Path testing is the strongest coverage criterion in white box testing. Finding target paths is a key challenge in path testing. Genetic algorithms have been successfully used in many software testing activities such as generating test data, selecting test cases and test cases prioritization. In this paper, we introduce a new genetic algorithm for generating test paths. In this algorithm the length of the chromosome varies from iteration to another according to the change in the length of the path. Based on the proposed algorithm, we present a new technique for automatically generating a set of basis test paths which can be used as testing paths in any path testing method. The proposed technique uses a method to verify the independency of the generated paths to be included in the basis set of paths. In addition, this technique employs a method for checking the feasibility of the generated paths. We introduce new definitions for the key concepts of genetic algorithm such as chromosome representation, crossover, mutation, and fitness function to be compatible with path generation. In addition, we present a case study to show the efficiency of our technique. We conducted a set of experiments to evaluate the effectiveness of the proposed path generation technique. The results showed that the proposed technique causes substantial reduction in path generation effort, and that the proposed GA algorithm is effective in test path generation.

Improvement of new automatic differential fuzzy clustering using SVM classifier for microarray analysis

In recent year, the problem of clustering in microarray data has been gaining significant attention. However most of the clustering methods attempt to find the group of genes where the number of cluster is known a priori. This fact motivated us to develop a new real-coded improved differential evolution based automatic fuzzy clustering algorithm which automatically evolves the number of clusters as well as the proper partitioning of a gene expression data set. To improve the result further, the clustering method is integrated with a support vector machine, a well-known technique for supervised learning. A fraction of the gene expression data points selected from different clusters based on their proximity to the respective centers, is used for training the SVM. The clustering assignments of the remaining gene expression data points are thereafter determined using the trained classifier. The performance of the proposed clustering technique has been demonstrated on five gene expression data sets by comparing it with the differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering and well known Fuzzy C-Means algorithm. Statistical significance test has been carried out to establish the statistical superiority of the proposed clustering approach. Biological significance test has also been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of genes. The processed data sets and the matlab version of the software are available at http://bio.icm.edu.pl/∼darman/IDEAFC-SVM/.