•A KNN algorithm is employed for diagnosing the stage of lung cancer disease.•A genetic algorithm is hybridized for an efficient feature selection.•The best value for K is determined using an ...experimental procedure.•The implementation on a long cancer database reveals 100% accuracy.•The proposed approach requires the least CPU time among four.
Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one could use the algorithm to find a correlation between the clinical information and data mining techniques to support lung cancer staging diagnosis efficiently.
The manual scheduling of medical treatment in a health centre is a complex, time consuming, and error prone task. Furthermore, there is no guarantee a manually generated schedule maximises the ...operational efficiency of the centre. Scheduling problems have seen extensive research across several domains. The current work presents a novel genetic algorithm for the scheduling of repetitive Transcranial Magnetic Stimulation (rTMS) appointments. The proposed List Scheduling Wildcard Tournament Genetic Algorithm (LSWT-GA) combines an innovative survivor selection policy with heuristic population initialisation. The algorithm aims to optimise the operational efficiency of a medical centre through efficient rTMS appointment scheduling. Additionally, the algorithm has the capacity to consider patient priority. Empirical experiments were conducted to evaluate the performance of the proposed algorithm, using a synthetic data set specifically developed to simulate the medical treatment scheduling problem. The experimental results showed the LSWT-GA algorithm outperforms other algorithms, obtaining the optimal makespan more frequently than a List Scheduling Genetic Algorithm (LS-GA) using traditional survivor selection policies and a standard genetic algorithm using random population initialisation (Random-GA). In addition to the novel genetic algorithm, LSWT-GA, the paper also makes a theoretical contribution by evaluating the run time of the LSWT-GA for makespan minimisation. The proposed algorithm and related findings can be applied directly to the administration systems in medical and healthcare centres and helps improve the deployment of medical resources for better treatment effect.
•A novel genetic algorithm, LSWT-GA, is presented for medical treatment scheduling.•LSWT-GA adopts survivor selection policy with heuristic population initialisation.•The evaluation of the LSWT-GA run time for makespan minimisation is promising.•An original synthetic data set is developed for medical scheduling optimisation.
•A self-learning GA (SLGA) which combines both SARSA and Q-learning with GA is first proposed to solve FJSP.•The combined model of SLGA is constructed according to the features of GA and RL.•SARSA ...algorithm and Q-learning algorithm of RL are combined, which constitute the main part of learning module in SLGA.•The components of RL are designed, including the state of GA environment, action of parameters adjustment, and reward method.•The mixedstrategy of SARSA algorithm and Q-learning algorithm improve the efficiency of SLGA for FJSP.
As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters cannot be dynamically adjusted effectively during the calculation process, which causes the solution efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the self-learning model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as the learning methods at initial and later stages of optimization, respectively, and the conversion condition is designed. Secondly, the state determination method and reward method are designed for RL in GA environment. Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA significantly outperforms its competitors in solving FJSP.
Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, ...the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies – point-to-point and Hub-and-spoke – with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies.
An enhanced diploid genetic algorithm (GA) is introduced for optimizing antenna arrays. Initially, several indicators are developed to measure the population state, thereby increasing the algorithm's ...responsiveness to variations in the antenna scheme. Subsequently, prevalent issues in antenna optimization are analyzed, leading to the introduction of a diploid GA aimed at preserving the diversity of antenna schemes, especially in a smaller population. This approach not only amplifies the exploration capacity of the algorithm but also addresses the issue of extensive simulation time. Furthermore, a local radial basis function (RBF) network is implemented for the assessment of some high-quality individuals, which effectively reduces the simulation count. In this method, the position of each individual in the solution space is considered the centroid, around which samples are selected for each antenna scheme to construct an individual RBF network. This technique simplifies the correlation between antenna parameters and performance, consequently decreasing the required sample size. Additionally, a local evolution acceleration mechanism is introduced to increase the convergence rate. The efficacy of the enhanced diploid GA is demonstrated through both test function experiments and a real-world application, showcasing its capability to efficiently optimize antenna arrays.
Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease ...in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm.
AI-aided financial regulation (AIFR) is a practical and significant task, but current solutions have yet to be optimized with customized model designs. Given the privacy concerns surrounding ...financial data, we aim to employ neural architecture search (NAS) to help nonexpert end-users automatically design architectures. The genetic algorithm (GA)-based NAS stands out due to its relatively low hardware requirements and robust theoretical foundation. However, constrained by limited data, the model would undergo architecture search on a general regulatory dataset while being deployed on private one owned by each organization. The data distribution of the private dataset may vary from that of public datasets, giving rise to the challenge of data domain shift. To alleviate this problem, we propose a novel fitness evaluation method. When scoring the fitness, we take into account both the architecture's validation accuracy and its potential for generalization by the metric of loss landscape. In addition, we improve the training paradigm for evaluation, utilizing a prototype-based training paradigm based on embedding distances for classification, allowing for rapid domain adaptation and improving performance on the distribution-shift data. We further introduce GA-TextCNN, a GA-based NAS framework specifically designed for text recognition, enhancing its suitability for text data within AIFR tasks. To demonstrate the effectiveness of our approach, we collect two related datasets and evaluate our method on it. The extensive experiments demonstrate that our method significantly improves baseline models and is effective in solving the AIFR problem.
This paper presents a Co-evolutionary Improved Genetic Algorithm (CIGA) for global path planning of multiple mobile robots, which employs a co-evolution mechanism together with an improved genetic ...algorithm (GA). This improved GA presents an effective and accurate fitness function, improves genetic operators of conventional genetic algorithms and proposes a new genetic modification operator. Moreover, the improved GA, compared with conventional GAs, is better at avoiding the problem of local optimum and has an accelerated convergence rate. The use of a co-evolution mechanism takes into full account the cooperation between populations, which avoids collision between mobile robots and is conductive for each mobile robot to obtain an optimal or near-optimal collision-free path. Simulations are carried out to demonstrate the efficiency of the improved GA and the effectiveness of CIGA.
The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power ...systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error.
•We propose a stochastic GASVM model for short-term forecasting of PV output power.•We use historical weather data collected from a local weather station for accurately modeling the system.•We evaluate the forecasting accuracy of the proposed technique based on RMSE and MAPE.•The results are compared with conventional SVM technique.•The proposed forecasting technique stands out for features like being robust, accurate, fast and needing less memory.
•Multiple attributes from IP flows are combined to detect anomalous events.•GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.•Unsupervised training technique applied ...efficiently for network traffic profiling.•Fuzzy Logic improved accuracy and false positives compared to state of art.
Due to the sheer number of applications that uses computer networks, in which some are crucial to users and enterprises, network management is essential. Therefore, integrity and availability of computer networks become priorities, making it a fundamental resource to be managed. In this work, a scheme combining Genetic Algorithm and a Fuzzy Logic for network anomaly detection is discussed. The Genetic Algorithm is used to generate a Digital Signature of Network Segment using Flow Analysis, where information extracted from network flows data is used to predict the networks traffic behavior for a given time interval. Furthermore, a Fuzzy Logic scheme is applied to decide whether an instance represents an anomaly or not, differing from some approaches present in the literature. Indeed, it is proposed an expert system with the capability to monitor the network’s traffic with IP flows while expected behaviors are generated in a regular time interval basis, issuing alarms when a possible problem is present. The proposed anomaly detection system exposes network problems autonomously. The results acquired from applying the proposed approach in a real network traffic flows achieve an accuracy of 96.53% and false positive rate of 0.56%. Moreover, our method succeeds in achieving higher performance compared to several other approaches.
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