Genetic Search Research Articles

Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

Combined and General Methodologies of Key Space Partition for the Cryptanalysis of Block Ciphers

This paper proposes two new methods of key space partitioning for the cryptanalysis of block ciphers. The first one is called combined methodology of key space partition (CoMeKSPar), which allows us to simultaneously set some of the first and last consecutive bits of the key. In this way, the search is performed using the remaining middle bits. CoMeKSPar is a combination of two methods already proposed in the scientific literature, the Borges, Borges, Monier (BBM) and the Tito, Borges, Borges (TBB). The second method is called the general algorithm of key space reduction (GAKSRed), which makes it possible to perform a genetic algorithm search in the space formed by the unknown bits of the key, regardless of their distribution in the binary block. Furthermore, a method of attacking block ciphers is presented for the case where some key bits are known; the basic idea is to deduce some of the remaining bits of the block. An advantage of these methods is that they allow parallel computing, which allows simultaneous searches in different sub-blocks of key bits, thereby increasing the probability of success. The experiments are performed with the KLEIN (Small) lightweight block cipher using the genetic algorithm.

Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

Background and Objective:The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions. Methods:This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model’s predictions are both accurate and comprehensible. Results:The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively. Conclusion:Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

Influence of heating elements layout on temperature uniformity in a large size heat treatment furnace

Computational Fluid Dynamics simulations were used to optimize heating elements placement on the walls of a large size electrical heat treatment furnace in order to reduce temperature gradients within the furnace and uneven temperatures on different faces of the blocks which could result in variabilities in final mechanical properties. Initial evaluations of different layouts highlighted the effectiveness of equipping two side walls. Subsequently, an optimal percentage of side wall coverage was identified through simulations and multi-objective evolutionary optimization. Genetic and pareto search algorithms yielded 10 % and 14.2 % as optimum values, respectively. Implementing an optimal coverage demonstrated significant reductions in surface temperature non-uniformity, surface-to-center temperature differential, and temperature differences between loaded blocks. These improvements suggest a potential for enhanced uniformity in mechanical properties across a batch of products. This approach presents a promising strategy for obtaining consistent and efficient heat treatment cycles in industrial heat treatment furnaces resulting in energy saving and improved product quality.

A Learner-Centric Explainable Educational Metaverse for Cyber–Physical Systems Engineering

Cyber–physical systems have become critical across industries. They have driven investments in education services to develop well-trained engineers. Education services for cyber–physical systems require the hiring of expert tutors with multidisciplinary knowledge, as well as acquiring expensive facilities/equipment. In response to the challenges posed by the need for the equipment and facilities, a metaverse-based education service that incorporates digital twins has been explored as a solution. However, the issue of recruiting expert tutors who can enhance students’ achievements remains unresolved, making it difficult to effectively cultivate talent. This paper proposes a reference architecture for a learner-centric educational metaverse with an intelligent tutoring framework as its core feature to address these issues. We develop a novel explainable artificial intelligence scheme for multi-class object detection models to assess learners’ achievements within the intelligent tutoring framework. Additionally, a genetic algorithm-based improvement search method is applied to the framework to derive personalized feedback. The proposed metaverse architecture and framework are evaluated through a case study on drone education. The experimental results show that the explainable AI scheme demonstrates an approximately 30% improvement in the explanation accuracy compared to existing methods. The survey results indicate that over 70% of learners significantly improved their skills based on the provided feedback.

Automatic molecular fragmentation by evolutionary optimisation

Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mol}^{-1}$$\\end{document}, respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mol}^{-1}$$\\end{document} for MBE2 and 24.3 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mol}^{-1}$$\\end{document} for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ mol-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mol}^{-1}$$\\end{document} were observed at the MBE2 and MBE3 levels, respectively.Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

Retraction Note: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Retraction Note: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Optimized k-nearest neighbours classifier based prediction of epileptic seizures

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.

A hybrid genetic tabu search algorithm for distributed job-shop scheduling problems

The distributed job-shop scheduling problem (DJSP) is an extension of the traditional job-shop scheduling problem, which are composed of two sub-problems, assigning jobs to suitable factories and deciding the operation sequence on machines. To evaluate the performance of algorithms for solving DJSP, several famous benchmark instances have been proposed, and most of these instances have not been solved so far. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for solving DJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. In GA part, a crossover operation and a mutation operation are devised based on the critical factory. The two operations can effectively improve the discreteness of the population. In TS part, a tabu search procedure is performed on the critical factory. The procedure can effectively enhance the local search ability of HGTSA. For evaluating the performance of HGTSA, it has been compared with five classical algorithms on 240 benchmark instances. The computational results show the efficiency and effectiveness of HGTSA for solving DJSP. In particular, the proposed HGTSA updates the upper bounds for 235 out of these difficult instances.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.

Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniques

AbstractWe present machine learning (ML) methods for automatically selecting a “best” performing fast algorithm for the capacitated vehicle routing problem (CVRP) with unit demands. Algorithm selection is to automatically choose among a portfolio of algorithms the one that is predicted to work best for a given problem instance, and algorithm configuration is to automatically select algorithm's parameters that are predicted to work best for a given problem instance. We present a framework incorporating both algorithm selection and configuration for a portfolio that includes the automatically configured “Sweep Algorithm,” the first generated feasible solution of the hybrid genetic search algorithm, and the Clarke and Wright algorithm. The automatically selected algorithm is shown here to deliver high‐quality feasible solutions within very small running times making it highly suitable for real‐time applications and for generating initial feasible solutions for global optimization methods for CVRP. These results bode well to the effectiveness of utilizing ML for improving combinatorial optimization methods.

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints.Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

Enhancing Fitness Evaluation in Genetic Algorithm-Based Architecture Search for AI-Aided Financial Regulation

Enhancing Fitness Evaluation in Genetic Algorithm-Based Architecture Search for AI-Aided Financial Regulation

Multi-objective optimization of HS-WEDM for hole cutting in thin-walled CFRP composites using COCOSO and genetic algorithms

This study aimed to enhance the cutting process parameters in molybdenum HS-WEDM of thin-walled carbon fiber composite (CFRP) products. Various hole quality characteristics were examined, including out-of-roundness, delamination factor, hole dimensional deviation, and cutting speed. The study utilized input parameters, including pulse-on duration, pulse-off duration, input current, and CFRP thickness. Optimization techniques, involving a second-order regression model and a genetic algorithm were used to individually optimize each hole characteristic. The combined compromise solution (COCOSO) method was employed to concurrently adjust the hole quality characteristics into a unified metric called the multiple performance criteria index (MPCI), with weight criteria for each response established using an entropy principle. The MPCI was then optimized using a genetic search algorithm. The findings reveal that the entropy, COCOSO, and genetic algorithm approaches efficiently optimize the HS-WEDM process parameters at various CFRP thicknesses to obtain precise hole quality characteristics. The implementation of these methods is expected to significantly improve hole quality, with enhancements of 29.57 % at 0.5 mm, 1.35 % at 1.0 mm, 9.65 % at 1.5 mm, and 6.05 % at 2.0 mm CFRP thicknesses.

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

AbstractWith the growing recognition of the unique advantages of reinforcement learning and genetic algorithms in addressing combinatorial optimization problems, this study aims to integrate these two methods to collectively tackle the classic combinatorial optimization challenge of the travelling salesman problem (TSP). The TSP stands as a quintessential combinatorial optimization challenge, tasked with determining the shortest path among designated cities. This paper introduces an innovative approach by amalgamating reinforcement learning's path selection prowess with genetic algorithms' global search strategy, aiming to uncover superior solutions in TSP. Specifically, the experiment employs a dual Q‐learning algorithm within reinforcement learning to identify multiple optimal paths, serving as progenitors for the genetic algorithm to further enhance performance. The paper meticulously outlines the problem modelling process, elucidating TSP instance definitions, descriptions, and precise objective function definitions. Experimental findings underscore the substantial enhancements achievable in TSP optimization through this comprehensive approach, offering a fresh perspective and methodology for tackling combinatorial optimization challenges.

Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study

Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.