Search

Geological drilling is an important means for exploration of the earth. Due to the nonlinearity and coupling, geological drilling is accompanied by low efficiency and safety, and difficult to accurately predict the drilling states. Moreover, the vertical well trajectory needs to follow the plumb line. A novel optimization method is proposed to solve these problems. First, working conditions are identified by fuzzy C-means clustering method and corresponding adjustment ranges of operating variables are refined for optimization. Meanwhile, support vector regression and hybrid bat algorithm are employed to construct reliable models for mud pit volume (MPV) and rate of penetration (ROP). Then, how to improve safety and efficiency is converted to a two-objective problem, that is to suppress MPV fluctuations and improve ROP with vertical well constraints. Nondominated sorting genetic algorithm II is invoked to solve this problem, and the appropriate solution is selected by two references points. Moreover, a short-time scale and long-time scale optimization strategy is introduced to cope with different adjustment interval of operating variables. Finally, the simulation results based on actual drilling data, application results in a semi-physical experiment system and comparison results from a vertical well verify the effectiveness and practicability for the developed method. [ABSTRACT FROM AUTHOR]

• We consider the objective empathetic relationships among experts. • The preferences of experts is handled based on their empathetic relationships. • We propose a method to determine the weights of experts based on ENA. • We propose a method to aggregate expert spatial information. • A large-scale group decision making method is proposed. Large-scale group decision making (LSGDM) involving a large number of experts has attracted more and more scholars' attention. Many LSGDM methods assumed that experts were independent to make evaluations, but the development of social media promotes the communication among experts, which makes experts no longer independent. In addition, existing LSGDM methods mainly adopted aggregation strategies such as the weighted average operator and arithmetic average operator to integrate the opinion of experts in a cluster, which makes the aggregation results cannot reflect the real opinion of the expert group. To address these issues, considering the empathetic network of experts, this study proposes an LSGDM method based on a new aggregation method for expert space information. Firstly, we determine objective weights of experts according to the objective empathetic relationships among experts. Then, the Steiner-Weber point problem is used as a prototype to establish an aggregation method called the spatial optimal aggregation (SOA) method to fuse the spatial information of experts. The model is solved by the genetic algorithm. Finally, an illustrative example about the selection of the most urgent risk in the transportation of COVID-19 vaccines is presented to show the validity and practicability of the proposed model. [ABSTRACT FROM AUTHOR]

Wind speed forecasting plays a crucial role in reducing the risk of wind power uncertainty, which is vital for power system planning, scheduling, control, and operation. However, it is challenging to obtain accurate wind speed forecasting results since wind speed series contain complex fluctuations. In this paper, a novel wind speed forecasting model is proposed by using a genetic algorithm (GA) and long short-term memory neural network (LSTM), where GA is used for evolving the architectures and hyper-parameters of the LSTM, called EvLSTM, because there is no clear knowledge to determine these crucial parameters. In the proposed EvLSTM forecasting model, a flexible gene encoding strategy, crossover operation, and mutation operation are proposed to describe the different architectures and hyper-parameters of the LSTM during the evolutionary process. In addition, to overcome the weakness of the single method for wind speed forecasting, the ensemble EvLSTM (EnEvLSTM) is proposed by using the no negative constraint theory ensemble learning strategy, whose weight coefficients are determined by the differential evolution algorithm. The proposed EvLSTM and EnEvLSTM forecasting models are evaluated on two real-world wind farms located in Inner Mongolia, China and Sotavento Galicia, Spain. Experimental results on two forecasting horizons demonstrate the superiority of EvLSTM and EnEvLSTM in terms of three performance indices and three statistical tests. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is proposed for population-based optimization algorithms' convergence. • DIRECT's partition and population evolutions are repeated in this framework. • The global convergence of the framework is proved. • Framework is applied successfully on three population-based optimization algorithms. Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version. [ABSTRACT FROM AUTHOR]

• A multi-objective profit-aware recommender system based on swarm intelligence. • A smart mutation operator has been designed and implemented for this problem. • The proposal has been compared with the only previous multi-objective approach. • The proposal has been also compared with collaborative filtering (a standard). • The experiments have been carried out based on different MovieLens datasets. Movie recommender systems are increasingly present in our daily lives, offering content of interest from streaming providers. Objectives in addition to the liking probability can be proposed to provide movie recommendations. However, there is a lack of recommenders that are aware of the benefit and that address the multi-objective nature of the problem. A profit-aware recommender system based on swarm intelligence in a multi-objective environment (multi-objective artificial bee colony, MOABC) has been designed, implemented, and applied. The proposed approach incorporates new intelligent operators that try to improve both objectives (liking probability and profit) simultaneously in each iteration instead of exclusively using randomness. This increases the quality of the recommendations with respect to the state-of-the-art algorithms. This new proposal has been evaluated using MovieLens datasets, covering different sizes (large, medium, and small). The experiments show that the MOABC performs better than collaborative filtering (CF, a standard in recommender systems) and Non-dominated Sorting Genetic Algorithm II (NSGA-II, the only multi-objective proposal in scientific literature that is profit aware) in terms of accuracy and global profit. Furthermore, statistical analysis shows that the proposed approach generates better and more robust results, also showing that the multi-objective nature of the problem must be exploited. [ABSTRACT FROM AUTHOR]

As environmental awareness grow, many organizations seek to implement Electric Vehicle (EV) fleets. Nonetheless, EVs' low driving ranges and high recharging times, and the limited Charging Stations (CS) availability make their management more challenging than conventional vehicles. The Electric Vehicle Routing Problem (E-VRP) tackles these challenges. However, many E-VRP variants drop relevant operational constraints, use overly simple models, or do not address route update solutions during operation. This work introduces a strategy to compute EV routes and update them according to observed traffic scenarios. By using an event-based EV state-space model, the strategy tracks relevant variables to account for multiple realistic elements, including nonlinear recharging function, partial recharging, mass-dependent energy consumption, maximum CS capacities, and time-dependent travel times. First, an Offline E-VRP (Off-E-VRP) variant is solved to find initial route candidates. Then, routes are periodically updated during operation according to traffic and EV state measurements by solving an Online E-VRP (On-E-VRP) variant. Genetic Algorithms (GA) are implemented to solve the problems via novel encoding and genetic operators. Finally, simulation results show that the strategy enables the fleet to fulfil its delivery duties, the pre-operation stage provides adequate initial route candidates, and the online stage can improve performance and service quality. [ABSTRACT FROM AUTHOR]

Mining high utility itemsets is an emerging and very active research area in data mining. The goal is to mine all itemsets with a utility value, in terms of importance to the user, no less than a predefined threshold value. Setting an appropriate threshold value is not trivial, requiring not only multiple trials but also the know-how in the application field. The advantage of algorithms for mining top- k high utility itemsets is they do not require such a utility threshold, but they suffer from very long runtimes and large memory requirements when large input data is considered. We propose a new genetic algorithm for mining top- k high utility itemsets, named TKHUIM-GA (Top-K High Utility Itemset Mining through Genetic Algorithms). It guides the search process by considering the utility of each item to produce initial solutions and to combine solutions accordingly, reducing the runtime and memory consumption as a result. A highly efficient data representation is utilized to reduce memory usage and runtime. A key advantage of TKHUIM-GA is that it works on positive, negative, integer and real unit utility values unlike existing approaches. Experiments on popular benchmark datasets demonstrate the high performance of the proposal regarding the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). UMOEAs-II, an improved version of UMOEAs, uses three differential evolution variants as multi-operator differential evolution (MODE), and CMA-ES. In this study, we further reform UMOEAs-II using an improved SHADE-cnEpSin that employs a novel adaptive strategy of scaling factor F , a crossover rate cr i , j updating mechanism which can calculate crossover rate for the i th individual with a particular j th component, an improved rank-based selective pressure based mutation strategy, and nonlinear population size reduction along with sequential quadratic programming method. The effectiveness of the improved rank-based selective pressure based mutation strategy, nonlinear population size reduction, and sequential quadratic programming are evident from the individual validations. The novel framework, enhanced the exploration and exploitation abilities, is named UMOEAs-III and is evaluated using the CEC2017 benchmark functions. The experiments are tested on 10, 30, 50, and 100 dimensions. The experimental results demonstrate the outstanding performance of UMOEAs-III in both low and high-dimensional tests compared to the state-of-the-art DE-based variants and hybrid algorithms. [ABSTRACT FROM AUTHOR]

United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). UMOEAs-II, an improved version of UMOEAs, uses three differential evolution variants as multi-operator differential evolution (MODE), and CMA-ES. In this study, we further reform UMOEAs-II using an improved SHADE-cnEpSin that employs a novel adaptive strategy of scaling factor F , a crossover rate cr i , j updating mechanism which can calculate crossover rate for the i th individual with a particular j th component, an improved rank-based selective pressure based mutation strategy, and nonlinear population size reduction along with sequential quadratic programming method. The effectiveness of the improved rank-based selective pressure based mutation strategy, nonlinear population size reduction, and sequential quadratic programming are evident from the individual validations. The novel framework, enhanced the exploration and exploitation abilities, is named UMOEAs-III and is evaluated using the CEC2017 benchmark functions. The experiments are tested on 10, 30, 50, and 100 dimensions. The experimental results demonstrate the outstanding performance of UMOEAs-III in both low and high-dimensional tests compared to the state-of-the-art DE-based variants and hybrid algorithms. [ABSTRACT FROM AUTHOR]

Some data mining, AI and data processing tasks might have data loss whose estimation/imputation is an important problem to be solved. Genetic algorithms are efficient and flexible global optimization methods able to deal with both multiple missing observations and multiple features such as continuous/discrete/binary data which are often found in multivariate databases unlike classical missing data estimation methods which only deal with univariate–continuous data. This paper presents a genetic algorithm to impute multiple missing observations in multivariate data which minimizes a new multi–objective (fitness) function based on the Minkowski distance of the means, variances, covariances and skewness between available/completed data. To do so, two sets of examples were tested: a continuous/discrete dataset which is compared to both the EM algorithm and auxiliary regressions, and a comparison over seven benchmark datasets. [ABSTRACT FROM AUTHOR]

• A method of personal privacy protection is proposed. • A genetic algorithm for community hiding is proposed. • A community hiding algorithm based on genetic algorithms using NMI is designed. Community structure plays an important role in social networks, which can reveal potential social relationships and deliver vast economic benefits to enterprises and organizations. Many efficient community detection algorithms have been proposed by researchers. However, effective community detection algorithms are accompanied by a growing problem of privacy disclosure. People have started to worry that their private information will be overexposed by community detection algorithms, so determining how to hide the community structure in the network to resist community detection algorithms has become an important issues. In view of this, we develop effective strategies to attack community detection algorithms through invisible disturbances to the network, namely, adding and removing a small number of connections, thus achieving privacy protection. In particular, a hiding strategy named "community hiding based on genetic algorithms using NMI (CGN)" is proposed in this paper. The algorithm uses normalized mutual information (NMI) as the fitness function and achieves an efficient global hiding effect by introducing a gene pool with prior information. We launched attacks based on CGN against four community detection algorithms on multiple real-world networks. By comparing with several state-of-the-art baseline algorithms, our CGN achieved the optimal results in NMI reduction. By visualizing the attack effect, it is proven that our CGN can achieve the community division error of nodes irrelevant to the connection changes by changing a very small number of connections, which fully reflects the concealment of community hiding. In addition, we further test the transferability and find that the modified network obtained by CGN on a specific community detection algorithm also shows extraordinary hiding effects when extended to other algorithms. [ABSTRACT FROM AUTHOR]

This paper explores the elliptic entropy and the elliptic semi-entropy of a coherent fuzzy number and discusses several of their properties. We propose a methodology that incorporates the adaptive index, k , of the coherent fuzzy number representing an individual investor's stock market assessment (pessimistic, optimistic, or neutral). We combine the adaptive index with the elliptic entropy and semi-entropy to obtain risk measures incorporating investor attitude. We use the proposed risk measures in a portfolio optimization problem that uses coherent fuzzy numbers to model the asset returns. The optimization models are solved using a genetic algorithm. We apply the proposed methodology to a large-scale case study involving 100 assets to demonstrate its effectiveness. The real-world performance of the proposed approach is illustrated using an out-of-sample analysis. Based on the results we obtained, we discuss the advantages and disadvantages of elliptic entropy and semi-entropy. Finally, we compare the proposed methodology with other approaches in the literature and demonstrate its superiority. [ABSTRACT FROM AUTHOR]

The outsourced distributed database is frequently used to tackle the large amounts of data in various smart city scenarios. The data partition technique is a significant research topic in the outsourced distributed database because it can directly affect the database performance in data exchange and sharing. Multiple objectives, including communication cost, load balance, and data privacy, should be considered during data partition. Previous approaches remain narrow in dealing with one of these objectives. However, the emphasis on any single objective cannot improve the entire performance of the outsourced distributed database. In this paper, a distributed segment-based genetic algorithm (DSGA) is proposed, which can protect data privacy as well as achieve the trade-off between communication cost and load balance. For privacy protection, a digit-based anonymity strategy is proposed based on the characteristic of attributes, which can maintain information integrity and achieve fuzzy identification. After that, a three-layer distributed framework is proposed for the multi-objective optimization to enhance the search efficiency and to achieve the trade-off between communication cost and load balance. Specifically, two segment-based operators, i.e., segment-based recombination and segment-based mutation, are proposed to sufficiently exchange evolutionary information to accelerate the convergence speed and to maintain population diversity to cover the whole multi-objective space, respectively. The performance of the proposed DSGA is verified in terms of solution accuracy and convergence speed. The effect of proposed strategies and operators in DSGA is also confirmed. [ABSTRACT FROM AUTHOR]

• Designed an intermodal hub-and-spoke logistic network with optional service levels. • Devised an improved tabu search algorithm with superior efficiency. • Applied the proposed model and algorithm to the case of China Railway Express. Designing the hub-and-spoke logistic network, a powerful structure in resource integration and cost reduction, has aroused ever-growing interest from scholars. Considering that, for realistic scenarios, customers' demands must take on greater diversity, in this paper, a hub-and-spoke network with differentiated services is developed, allowing clients to choose their preferred service levels, while considering multiple transportation modes, with environmental parameters and economies of scale incorporated into the modeling process. Further, to solve the derived optimization problem effectively, an improved tabu search algorithm is proposed, in which an appropriate memory function is included to strengthen its adaptability. The improved algorithm is found to have outstanding efficiency when compared to arithmetic software and the genetic algorithm, for both small and large size instances. Finally, the theoretical findings are applied to the case of China Railway Express, to redesign the logistic network in a road-rail intermodal transportation mode and support decision-making over hub location and allocation. Our findings offer important practical insights that can aid contributions to evidence-informed policy formulation and implementation. [ABSTRACT FROM AUTHOR]

The LPN problem, lying at the core of many cryptographic constructions for lightweight and post-quantum cryptography, has recently received much attention. Accordingly, BKW, Gauss algorithm, and their improved or hybrid variants have been proposed to solve the LPN problem. In this paper, we propose an intelligent method for LPN solving from a wholly new perspective by iterative correcting the secret, which can successfully reduce the dimension of the LPN problem. While the challenge is that there are so many independent secret variables of the given checking parity equations with noise in LPN problems, a set of rules should be developed to effectively direct and correct the secret. To solve this problem, we skillfully introduce the genetic algorithm to simulate the process of iterative correction and further add vaccination technology to guide and speed up the iterative process. Owing to the small memory and data consumption of our algorithm, we conducted experiments and, for the first time, solved the largest practical LPN (256,1/8) instance in 30 days, which shows the superiority of our method. To the best of our knowledge, this is the first time the iterative correction method and the intelligent algorithm have been successfully applied to LPN problems. [ABSTRACT FROM AUTHOR]

Graph-based models for Electrocardiogram (ECG) incorporate physiological spatial information among ECG leads in elegant manners. It is unachievable for convolution-based models. Nevertheless, existing ECG graph models heavily rely on manually pre-designed structures. Such structures may not be optimal for all ECGs. Moreover, these models lag behind larger representative models, despite their superiority in lightweight cases. In this study, a novel graph structure learning method is proposed. It can learn optimal structures and enhance model representations simultaneously. Typically, ECGs belong to limb/chest lead systems. To explore inter-system and intra-system connections, heuristic optimization including genetic algorithm and particle swarm optimization is employed. Specifically, candidate nodes/edges are encoded as individuals. Thereby graph structure learning is transformed into population iterations. In this scenario, graph-level embedding becomes non-applicable because models require retraining once structures change. Instead, node-level embedding continuously enhances models during structure learning. Consequently, Honest-GE further exploits graph methods' superiority in lightweight cases. It outperforms state-of-the-art models 11.53% and 10.03% in F 1 on two databases. Additionally, it demonstrates comparable performance with larger models. In general, graph learning results corroborate medical knowledge and offer insights for lead selection. Honest-GE provides a promising avenue for high-performing lightweight models and portable deployments with fewer leads. • Heuristic algorithms are applied for Electrocardiogram graph structure learning. • Node-embedding GraphSAGE is employed to enhance model continuously in searching. • Honest-GE further shows superiority over existing methods with similar parameters. • Honest-GE achieves competent performance against representative large models. • Result corroborate with medical knowledge and provide advice for lead selection. [ABSTRACT FROM AUTHOR]

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller. [ABSTRACT FROM AUTHOR]

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. [ABSTRACT FROM AUTHOR]

• Jointly consider the task dependency and edge cloud environment. • Combine deep reinforcement learning and genetic algorithm. • Generate the initial population of genetic algorithm by DQN. Edge computing can greatly decrease the delay between users and cloud servers, which can significantly improve system service performance. However, it remains challenging for more efficient scheduling and allocation of users' application demands with dependence constraints to edge cloud servers. Due to the randomness of the initial population, traditional intelligent optimization algorithms have poor convergence speed in addressing resource scheduling. Therefore, to minimize the execution time of the application, this paper proposes a hybrid algorithm to solve the resource scheduling problem with parallelism and subtask dependency. To improve the convergence speed of the algorithm, this paper makes full use of the features of deep Q networks (DQN) and genetic algorithms (GA). The initial population of GA is generated using DQN. Finally, to evaluate the effectiveness of our proposed algorithm, this paper selects three real scientific workflows for experiments. The experimental results show that the hybrid algorithm can converge quickly and improve the optimization effect in a short time. [ABSTRACT FROM AUTHOR]

Owing to the rapid development of cloud services and personal privacy requirements, content-based encrypted image retrieval in the cloud has been increasing. Outsourced images are encrypted into noiselike ones to protect privacy, however, the obtained unrecognized appearance limits their availability. Besides, users have to decrypt all search results to browse, while some of them may not be needed, which undoubtedly wastes bandwidth and computing resources. To cope with this problem, a compromise strategy is proposed that considers the tradeoff between privacy and usability of cipher images. Wherein, a thumbnail preserving encryption (TPE) based on genetic algorithm is proposed. The pixels in the sub-blocks of the plain image are scrambled and diffused at the bit-level through crossover and mutation operators of the genetic algorithm. Moreover, two new operators of Mutation Compensation and Mutation Failure are defined and incorporated into the traditional genetic algorithm to achieve an ideal TPE, that cipher image has the same thumbnail as the original image. Additionally, a color histogram-based retrieval algorithm is introduced to retrieve cipher images using the color information preserved by thumbnails; and to improve retrieval accuracy by using the Bhattacharyya distance. A series of simulations verify the security and effectiveness of our scheme. [ABSTRACT FROM AUTHOR]

• A novel method, TITL-MARS-OPT, for global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines is proposed. • The results show the superiority of TITL-MARS-OPT over the genetic algorithm and the gradient descent method on TITL-MARS models. • The Python code is made open-source on GitHub to help other researchers. Multivariate adaptive regression splines (MARS) has been adopted as a popular surrogate function to model the unknown relationships between input and output variables in complex systems. Searching optimal solutions on the complicated surrogate response surface of MARS serves as important tasks in various applications. In this paper, we present an efficient and effective approach to find a global optimal value of MARS models that incorporate two-way interaction terms which are products of truncated linear univariate functions (TITL-MARS). Specifically, with a MARS model consisting of linear and quadratic structures, we can reformulate the optimization problem on TITL-MARS into a mixed integer quadratic programming problem (TITL-MARS-OPT), which can be further solved in a more principled way. To illustrate the effectiveness of our proposed approach TITL-MARS-OPT, we come up with a genetic algorithm and a gradient descent algorithm to solve the original version of TITL-MARS, and we compared the performance of the proposed approach with the benchmark algorithms. Numerical experiments are conducted on a spectrum of examples with different levels of complexity, including 6 existing models and a real world application in the wind farm optimization. Our proposed approach can find a global optimal successfully and efficiently while the comparison algorithms fail to find a global optimal solution. In the end, Python code for TITL-MARS and TITL-MARS-OPT is made available on GitHub (https://github.com/JuXinglong/TITL-MARS-OPT). [ABSTRACT FROM AUTHOR]

• A novel method, TITL-MARS-OPT, for global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines is proposed. • The results show the superiority of TITL-MARS-OPT over the genetic algorithm and the gradient descent method on TITL-MARS models. • The Python code is made open-source on GitHub to help other researchers. Multivariate adaptive regression splines (MARS) has been adopted as a popular surrogate function to model the unknown relationships between input and output variables in complex systems. Searching optimal solutions on the complicated surrogate response surface of MARS serves as important tasks in various applications. In this paper, we present an efficient and effective approach to find a global optimal value of MARS models that incorporate two-way interaction terms which are products of truncated linear univariate functions (TITL-MARS). Specifically, with a MARS model consisting of linear and quadratic structures, we can reformulate the optimization problem on TITL-MARS into a mixed integer quadratic programming problem (TITL-MARS-OPT), which can be further solved in a more principled way. To illustrate the effectiveness of our proposed approach TITL-MARS-OPT, we come up with a genetic algorithm and a gradient descent algorithm to solve the original version of TITL-MARS, and we compared the performance of the proposed approach with the benchmark algorithms. Numerical experiments are conducted on a spectrum of examples with different levels of complexity, including 6 existing models and a real world application in the wind farm optimization. Our proposed approach can find a global optimal successfully and efficiently while the comparison algorithms fail to find a global optimal solution. In the end, Python code for TITL-MARS and TITL-MARS-OPT is made available on GitHub (https://github.com/JuXinglong/TITL-MARS-OPT). [ABSTRACT FROM AUTHOR]

• Data lakes store unprocessed business data at large scale. • Clustering helps data engineers understand the structure of their data lakes. • RóMULO is a meta-heuristic multi-way clustering proposal to cluster data lakes. • The results confirm that RóMULO is a promising contribution to assist data engineers. Data engineers are very interested in data lake technologies due to the incredible abundance of datasets. They typically use clustering to understand the structure of the datasets before applying other methods to infer knowledge from them. This article presents the first proposal that explores how to use a meta-heuristic to address the problem of multi-way single-subspace automatic clustering, which is very appropriate in the context of data lakes. It was confronted with five strong competitors that combine the state-of-the-art attribute selection proposal with three classical single-way clustering proposals, a recent quantum-inspired one, and a recent deep-learning one. The evaluation focused on exploring their ability to find compact and isolated clusterings as well as the extent to which such clusterings can be considered good classifications. The statistical analyses conducted on the experimental results prove that it ranks the first regarding effectiveness using six standard coefficients and it is very efficient in terms of CPU time, not to mention that it did not result in any degraded clusterings or timeouts. Summing up: this proposal contributes to the array of techniques that data engineers can use to explore their data lakes. [ABSTRACT FROM AUTHOR]

Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel production systems. This paper considers a width deviation prediction problem and proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to obtain a prediction model. Existing work mainly focuses on high prediction accuracy, while ignoring interpretability. This work aims to build a prediction model that can make a good trade-off between two industry-required criteria, i.e., prediction accuracy and interpretability. It first collects some process variables in a hot rolling process and includes them as well as some constructed variables in a feature pool. Then we propose MGH to find representative variables from it and build a prediction model. MGH results from the integration of hierarchical clustering, genetic algorithm, and generalized linear regression. In detail, hierarchical clustering is applied to divide variables into clusters. Genetic algorithm and generalized linear regression are innovatively combined to select a representative variable from each cluster and develop a prediction model. The computational experiments conducted on both industrial and public datasets show that the proposed method can effectively balance prediction accuracy and interpretability of its resulting model. It has better overall performance than the compared state-of-the-art models. [ABSTRACT FROM AUTHOR]

Community detection algorithms play an important role in revealing and analyzing complex network structures. However, when these algorithms are put into practice, privacy concerns often arise, especially when community nodes wish to safeguard their identities from exposure. This interaction between network analysis and privacy issues leads to what we refer to as the "community hiding problem." In the context of concealing specific target communities, popular methods primarily focus on preventing community detection algorithms from accurately identifying the entire original community, resulting in partial concealment. This study formally addresses the challenge of achieving complete concealment of target communities. We introduce key metrics such as escape scores, dispersion scores, and hiding scores to precisely define the problem. Additionally, we design a metric, denoted as M-value, to evaluate the effectiveness of concealing target communities from multiple perspectives. To tackle this challenge, we employ a genetic algorithm that leverages previous knowledge to maximize the concealment of the target community while minimizing changes to link connections. We validate our approach through extensive experiments with various real-world datasets, demonstrating that our algorithm outperforms several state-of-the-art baseline algorithms across multiple metrics. Visual representations of our method confirm its ability to effectively hide target communities while minimally affecting the broader network's community structure, strengthening the inherent effectiveness of our community hiding approach. Furthermore, we assess the adaptability of our modified network with different community detection algorithms, consistently demonstrating effective concealment even when these algorithms are extended. This emphasizes the robustness and generality of our proposed approach in various algorithmic scenarios. [ABSTRACT FROM AUTHOR]

Attribute reduction is a crucial issue of rough set theory and has been applied to various fields. It aims to remove useless or redundant features from data and extract precise rules. Traditional attribute reduction methods have obvious limitations, that is, it can not guarantee the generalization ability of reduced decision rules to classification but can only guarantee the classification performance to visible objects. To overcome this issue and enhance the model classification generalization ability without increasing rules' complexity based attribute reduction, we propose a novel attribute method by employing the Structural Risk Minimization principle, which is a classic method in machine learning to balance model complexity and performance. The improved attribute reduction method with rough set tries to get a trade-off between the number of features and empirical error, wherein the former is defined by rule confidence, and the latter is explored by the mutual information between the condition attribute subset and the decision attribute. In other words, model accuracy can be regarded as an empirical error term to measure the model's classification or prediction performance, and the model complexity as a penalty term to characterize the size of the reduction subset. To implement this approach, genetic algorithm is employed as a heuristic search technique, to obtain the optimal reduction subset. Several comparative experiments are performed on ten UCI datasets to evaluate the model's classification accuracy and the size of the reduction subset. The experimental results indicate the method proposed in this paper with better generalization ability compared to other traditional algorithms under the condition of equal reducted set length. • We propose a new attribute reduction method by considering the model accuracy and complexity simultaneously. • We use rule confidence and mutual information to define the model's accuracy and complexity, respectively. • We employ the genetic algorithm as a heuristic search technique to obtain the optimal reducted subset. [ABSTRACT FROM AUTHOR]

In the modern landscape, the fusion of forecasting and computational intelligence empowers organizations to extract invaluable insights from vast datasets, facilitating informed decision-making, swift adaptation to market dynamics, and the enhancement of competitiveness, ultimately fostering innovation. Notably, forecasting has recently garnered significant attention, particularly within the realm of big data. This modern terrain presents a dual challenge: effectively distilling essential insights from complex data and optimizing data utilization. To address this challenge, we introduce a pioneering forecasting model (FM) engineered to excel in both information extraction and data utilization. Our approach commences with a formula designed to compute feature similarity, leveraging trend change data. These similarity metrics, applied to features and their relationships with labels, inform our feature selection process. This method integrates spatial and temporal correlations among features, fostering robust interconnections within the selected subset. Subsequently, we employ a feedforward neural network (FNN) to optimize feature subsets based on test error. While the FM trained with the optimal feature subset exhibits enhanced performance, it still shows some deviation from actual values in initial forecasting results (FORs). To mitigate these discrepancies, we implement a data utilization strategy. We refine the initial FORs by considering the contribution of each feature subset to them, thereby reducing disparities between forecasts and actual values. Model parameters are fine-tuned using a genetic algorithm (GA). Finally, we conduct an experimental analysis, comparing our FM to seven similar models using publicly available datasets. Our experimental results consistently demonstrate that our FM achieves high forecasting performance (FOP). [ABSTRACT FROM AUTHOR]

Recently, graph data become increasingly important and larger in many fields, and many distributed graph computing systems have been proposed to deal with large-scale graphs. The graph partition algorithm is the basis of these systems. In many graph applications, a vertex updates its feature by aggregating information of its multi-order neighboring vertices, and the existing graph partition algorithms often lead to heavy communication cost. This paper proposes a graph partition algorithm to store the multi-order neighbor vertices redundantly to avoid the communication requirement. It first formulates the problem of graph partitioning with redundant multi-order neighbor vertices storage as an optimization problem, and then proposes PARN (partition algorithm with redundant multi-order neighbor) algorithm to tackle it. The PARN algorithm consists of initial partition and vertex migration based on genetic algorithm, and it classifies the vertices of a partition into primary and auxiliary vertices. The auxiliary vertices required for the primary vertices in a partition are stored in the same partition, so the primary vertices don't need to fetch the information from the other partitions. The experiment results show that the proposed algorithm is comparable to Hash, GAP, GNP, and QCLP algorithms in terms of load balance and produces fewer auxiliary vertices than these algorithms. • Formulating the graph partitioning problem with redundant storage of multi-order neighbor vertices. • Presenting a graph partitioning algorithm PARN to solve this problem. • Performing extensive experiments to show the out-performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter λ between the two objectives. However, finding a good value of λ is not easy because of its large parameter space; also, there may exist many equally good λ s. In this paper, a nondominated sorting genetic model (NSGC) is proposed to tackle the co-clustering problem, totally bypassing the trade-off parameter λ and returning to the original two-objective problem. The core of our model is to group a row objective function and a column objective function and integrate them into a genetic algorithm as the fitness functions. After this reformulation, we follow a standard genetic algorithm procedure to iteratively find the Pareto-optimal solutions. Finally, to fish out a single best solution we further design a sorting criterion according to which the Pareto-optimal solution set can be totally ordered. Extensive experiments with 16 public datasets are conducted, and the results demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

• A hybrid solution that uses a genetic algorithm and models property features as fuzzy partitions is proposed for selecting the residential properties whose economic value is most consistent with the purchase offer. • Our framework does not need a learning phase such as in Machine Learning real estate price assessment models. • Expert knowledge is modeled using fuzzy partitioning of the feature domains and evaluating positive or negative deviation value from the average unit price. • The reliability of the solution is evaluated by measuring its fuzziness. Evaluating the actual price of a residential property is a critical issue in the real estate market. Real estate market practitioners gauge a property's price by considering features such as property type and residential area. Subsequently, they evaluate the property's intrinsic features, such as condition, sun exposure, scenic views, and ancillary amenities. Finally, extrinsic features such as the proximity of services and infrastructure are assessed. This paper proposes a new genetic approach for selecting residential properties that meet the purchase offer and the intrinsic and extrinsic characteristics desired by the client. Since the real estate market's changes can influence extrinsic features, the method introduces price fluctuations of properties. Extrinsic features are modelled as fuzzy partitions: each fuzzy set describes a qualitative aspect of the corresponding feature that, expressed in a linguistic term, has a human-like interpretation. Then, a deviation value (fluctuation) from the average price of the property is considered for each fuzzy set in the partition. All the property features, extrinsic and intrinsic, are encoded in the chromosome genes of the genetic algorithm. The fitness function calculates the distance between the unit price of the property and the purchase offer. Some case studies were conducted in various Italian municipalities, using the average price per square meter of residential properties the Osservatorio del Mercato Immobiliare (OMI) assigned. Depending on customer requirements and preferences, different OMI zones were selected using additional characteristics such as type, location, conservation, and proximity to various urban services. The results demonstrated the effectiveness of the proposed approach for all the case studies, showing how the optimal solution represents a good compromise between customer preferences and market offerings. [ABSTRACT FROM AUTHOR]

The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on "gaps" and reducing some PFa regions' sampling too frequently. Such a balancing mechanism allows B-NTGA to be more adaptive and focus on less explored PFa regions. The proposed B-NTGA is investigated on two benchmark multi- and many-objective optimization real-world problems, like Thief Traveling Problem and Multi-Skill Resource-Constrained Project Scheduling Problem. The results of experiments show that B-NTGA has a higher efficiency and better performance than state-of-the-art methods. • New balanced GAP selection actively uses archive, focuses on Pareto Front 'gaps' to reduce too frequent sampling of regions. • B-NTGA bases and modifies NTGA2 (i.e., clone elimination, tournament as second selection) and adds balanced GAP selection. • B-NTGA is less time-consuming than NTGA2, uses fewer parameters, and is more efficient than NTGA2. • Experiments for NP-hard multi-objective optimization problems (TTP and MS-RCPSP) comparing B-NTGA to state-of-the-art methods. • Results of experiments showed that B-NTGA is highly effective for multi- and many-objective problems (like TTP and MS-RCPSP). [ABSTRACT FROM AUTHOR]

Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence. In this paper, an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed. In the ASDNSGA-II, the strategy with a special congestion degree is used to improve the selection strategy. Then a new adaptive crossover strategy is designed by evaluating the advantages and disadvantages of the SBX crossover strategy with the ability to solve high dimensions and the BLX-α with the ability of Pareto solution to produce offspring solutions. These can ensure the generation of offspring solutions around individuals with large crowding degrees and balance the convergence and diversity of decision space and object space. It can improve PS distribution and convergence and maintain PF precision. Eight functions of MMF1-MMF8 from the CEC2020 are selected to prove the effectiveness of the ASDNSGA-II. By comparing several latest multi-modal multi-objective evolutionary algorithms, the results show that the ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of PS. [ABSTRACT FROM AUTHOR]

[Display omitted] Data selection, which includes feature and instance selection, is often an important step in building prediction systems. Genetic algorithms (GA) frequently allow finding better solutions than classical methods in many areas. This is also true for the instance selection task. The main difficulties and challenges in GA-based instance selection are high computational complexity and decreasing performance with the dataset size growth. This is caused by the fact that each instance is encoded in one chromosome position. Hence bigger datasets result in longer chromosomes. The main contribution of this paper addresses the above problems in a three-step process. In the first step the dataset is divided into several consistent regions by fuzzy clustering. Then GA-based instance selection is performed independently within each cluster. Finally ensemble voting provides seamless aggregation of the partial results from the overlapping clusters. This improves dataset exploitation by more localized search and also takes the advantage of ensemble methods. This method significantly improves the predictive model performance and data reduction in comparison to instance selection performed on the whole training dataset. [ABSTRACT FROM AUTHOR]

Generally, classification problems catalog instances according to their target variable without considering the relation among the different labels. However, there are real problems in which the different values of the class are related to each other. Because of interest in this type of problem, several solutions have been proposed, such as cost-sensitive classifiers. Ensembles have proven to be very effective for classification tasks; however, as far as we know, there are no proposals that use a genetic-based methodology as the metaheuristic to create the models. In this paper, we present OCEAn, an ordinal classification algorithm based on an ensemble approach, which makes a final prediction according to a weighted vote system. This weighted voting takes into account weights obtained by a genetic algorithm that tries to minimize the cost of classification. To test the performance of this approach, we compared our proposal with ordinal classification algorithms in the literature and demonstrated that, indeed, our approach improves on previous results. [ABSTRACT FROM AUTHOR]

• It is possible to find partitions that outperform global and local approaches. • An exhaustive partitions search is not be feasible with a large number of outputs. • Genetic algorithms help accelerate the search but do not always outperform baselines. • Finding an optimal partitioning may lead to overfitting. In multi-target prediction, an instance has to be classified along multiple target variables at the same time, where each target represents a category or numerical value. There are several strategies to tackle multi-target prediction problems: the local strategy learns a separate model for each target variable independently, while the global strategy learns a single model for all target variables together. Previous studies suggested that the global strategy should be preferred because (1) learning is more efficient, (2) the learned models are more compact, and (3) it overfits much less than the local strategy, as it is harder to overfit on several targets at the same time than on one target. However, it is not clear whether the global strategy exploits correlations between the targets optimally. In this paper, we investigate whether better results can be obtained by learning multiple multi-target models on several partitions of the targets. To answer this question, we first determined alternative partitions using an exhaustive search strategy and a strategy based on a genetic algorithm, and then compared the results of the global and local strategies against these. We used decision trees and random forests as base models. The results show that it is possible to outperform global and local approaches, but finding a good partition without incurring in overfitting remains a challenging task. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of vehicles (IoV) technology becomes a research hotspot. However, it also becomes a hotbed for malicious attacks. In the IoV, frequent data transmission and complex connections among numerous different nodes increase the complexity and diversity of malicious attacks. In order to realize the accurate and rapid detection of malicious attacks in the IoV environment, in this paper, an intrusion detection method is proposed by combining oversampling, outlier detection and metric learning. The proposed approach improves intrusion detection effect in three main ways: 1) it oversamples the minority classes based on a novel strategy, 2) it introduces a new feature with basis of imbalance ratio, and 3) it reduces the outliers and rescales original samples actively to make the decision boundary clearer by combining outlier detection and distance metric learning. Furthermore, genetic algorithm is used to extract the optimal subset of features. The experimental results show that the proposed method can achieve 98.51% accuracy and maintain 0.82% false alarm rate on UNSW-NB15 dataset. Also, it outperforms the existing methods on ROAD, Car-Hacking and CAN-intrusion dataset for in-vehicle communications. [ABSTRACT FROM AUTHOR]

• Development of a novel mathematical model PCRE. • Proposal of a novel cascade hybrid many-objective recommendation method CHMAOR. • Design of a multiparent probabilistic heuristic genetic algorithm MPPHX. • Comparison of the proposed algorithms with state-of-the-art methods. • Statistical testing to evaluate the efficiency of the proposed algorithms. Most previous studies of recommender systems (RSs) have particularly focused on optimizing user experience; however, users are not the only stakeholders of an RS. A pure concentration of users limits the ability to incorporate the perspectives of other stakeholders, such as providers. Furthermore, because users' preferences and providers' objectives may conflict, considering only users' views degrades the recommendation methods' utility. Therefore, we propose a cascade hybrid many-objective recommendation method (CHMAOR) to balance four objectives for two different stakeholders. CHMAOR combines provider coverage (PC), user reach coverage (URC), and provider entropy (PE) to create a new provider visibility model (PCRE). The many-objective optimization (MOP) stage includes a novel multiparent probabilistic heuristic genetic algorithm (MPPHX) that heuristically considers both parents' gene frequency and recommendation list features. Extensive experiments demonstrate the following. 1) CHMAOR effectively balances user and provider objectives in terms of accuracy, diversity, novelty, and provider visibility according to the baseline algorithms. 2) The PCRE model considers not only provider coverage but also provider appearance frequency and provider diversity while effectively changing imbalanced provider recommendations. Furthermore, PCRE dramatically reduces the complexity of high-dimensional many-objective recommendations. 3) Our MPPHX achieves better convergence and diversity solutions than the competing MOP algorithms. [ABSTRACT FROM AUTHOR]

Zero residual vibration (RV) in the position control of a flexible system is difficult to achieve because this system has low stiffness and underactuated feature. In this paper, we present a position control strategy to achieve zero RV in the position control of two degrees-of-freedom (DOF) flexible systems. First, a general dynamic model for two-DOF flexible systems is established. Next, we transform the position control with zero RV to motion planning and tracking control. Based on the position control objective with zero RV, three constraints for the desired system trajectory are given. To make the desired system trajectory satisfy these three constraints, we propose a motion trajectory optimization method. Specifically, we plan a forward system trajectory and a reverse system trajectory based on bidirectional trajectory planning method. Then, the rewinding strategy and the trajectory optimization based on genetic algorithm are used to connect these two system trajectories and to obtain a complete desired system trajectory. Finally, a sliding mode tracking controller is designed to make the system track this desired system trajectory. In this way, the system can reach its target-rest-position with zero RV. The effectiveness and superiority of the proposed control strategy are demonstrated in simulations. [ABSTRACT FROM AUTHOR]

This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience® initiative. As shown in the experimental section, the proposed quantum genetic algorithm's performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy. [ABSTRACT FROM AUTHOR]

• This paper focuses on the tourist trip design problem in a fuzzy environment. • A rough approximation based model is proposed to deal with the fuzzy variables. • A hybrid genetic algorithm is designed to search the optimal route. • Our approach performs significantly better than current methods. • The evolution strategies based on the improved PSO can achieves better efficiency. Trip planning significantly improves tourists' experiences and enhances the competitive advantage of tourist attractions. We focus on the tourist trip design problem (TTDP) under a fuzzy environment, which is an extension of TTDP that considers the spatiotemporal route structure and variable sightseeing value at points of interest. A rough approximation-based model is proposed to deal with fuzzy variables, and a hybrid genetic algorithm is designed to identify the optimal route. We conduct a numerical experiment to assess the performance of the presented approach. The results of the Wilcoxon rank sum tests indicate that our approach performs significantly better than currently available methods. The evolution strategies based on improved particle swarm optimization also demonstrate better efficiency than existing approaches. [ABSTRACT FROM AUTHOR]

• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process. [ABSTRACT FROM AUTHOR]

Train energy-efficient control involves complicated optimization processes subject to constraints such as speed, time, position and comfort requirements. Conventional optimization techniques are not apt at accumulating numerous solution instances into decision intelligence by learning for consecutively confronted new problems. Deep reinforcement learning (DRL), which can directly output control decisions based on current states, has shown great potentials for next-generation intelligent control. However, if the DRL is directly applied to energy-efficient train control, the received results are almost unsatisfactory. The reason lies in that the agent may get into confusion about how to trade off those constraints, and spend great computational time performing a large number of meaningless explorations. This article attempts to propose an approach of DRL with a reference system (DRL-RS) for proactive constraint handling, where the reference system deals with checking and correcting the agent's learning progresses to avoid stepping farther and farther onto the erroneous road. The proposed approach is evaluated by the numerical experiments on train control in metro lines. The experimental results demonstrate that the DRL-RS can achieve faster learning convergence, compared with the directly applied DRL. Furthermore, it is possible to reduce more energy consumption than the commonly used genetic algorithm. [ABSTRACT FROM AUTHOR]

• We use several thresholds to convert different version of the data with varying certainty. • A random surfer model is employed to compute similarity values using higher order correlations. • Genetic algorithm is used as an optimization technique with the addition of a local search heuristic. • Our experiments show that using the proposed approach gives much better results even when uncertainly is quite high in the data. Probabilistic graphs have gained much interest in the data mining community since the big data revolution. Graph clustering is a widely used technique in exploratory data analysis such as data compression, information retrieval, protein–protein interaction network, etc. Traditional clustering algorithms assume that the data represented in a graph is deterministic, i.e., the edges between nodes are certain, though they may vary in their importance (weight). In probabilistic graphs, however, edges are uncertain and are represented as probabilities. In this work, we propose an evolutionary algorithm based clustering technique specifically to deal with uncertainties in such data. It has been observed that correlation usually exists among adjacent edges. Our work exploits this principle and explores the paths between nodes (including those of multiple order) to provide an estimate of the similarity between nodes with probabilistic edges. We embed this information into the fitness function and use Genetic algorithm to optimize the solution. The algorithm is tested on several benchmark datasets and compared with recent state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Feature selection is the process of selecting an optimal subset of features required for maintaining or improving the performance of data mining models. Recently, hybrid filter/wrapper feature selection methods have shown promising results for high-dimensional data. However, filter/wrapper methods lack of generalisation power, which enables the selected features to be trainable over different classifiers without having to repeat the feature selection process. To address the generalisation power problem, this paper proposes a novel evolutionary-based filter feature selection algorithm that is sequentially hybridised with the Fisher score filter algorithm in a new hybrid framework called filter/filter. The proposed algorithm is based on a long-term memory Tabu Search combined with an Asexual (i.e. mutation-based) Genetic Algorithm (TAGA). TAGA benefits from a new integer-encoded solution representation, a novel mutation operator, a new tabu list encoding scheme, and uses a minimum redundancy maximum relevance information theory-based criterion as the fitness function. Experiments were carried out on various high-dimensional datasets including image, text, and biological data. The goodness of the selected subsets was evaluated using different classifiers and the experimental results demonstrate that TAGA outperforms other conventional and state-of-the-art feature selection algorithms. [ABSTRACT FROM AUTHOR]

Data streams applications generate a continuous stream of data in a high rate that it is not possible to store all data in available memory. Hence, it is important to apply techniques that are capable of learning concepts according to data presentation, taking into consideration available time, processing and memory resources. This paper presents a new ensemble-based approach to detect concept drift in the data stream context. This approach uses an explicit mechanism to adapt to concept drifts using a genetic algorithm in order to define the best ensemble configuration for the current scenario (detected drifts). The main aim of this approach is to provide an efficient structure to detect and to adapt to concept drifts and, as a consequence, to improve the performance of this system. Our findings show that the proposed method delivers outstanding results, outperforming the results delivered by the most efficient methods presented in the literature. [ABSTRACT FROM AUTHOR]

• We provide efficient algorithms for constructing an incomplete compromise ranking. • We account for the utilitarian and egalitarian settings w.r.t. multiple input rankings. • The algorithms are based on various metaheuristics applicable to large-scale problems. • We report the results of a case study concerning the ranking of insulating materials. • Extensive experimental comparison indicates the best-performing algorithms. We propose heuristics for constructing a compromise incomplete ranking based on partial rankings admitting incomparability. We consider the utilitarian and egalitarian perspectives oriented toward minimizing an average or a maximal distance from any input ranking. The proposed algorithms incorporate genetic algorithms, simulated annealing, tabu search, local search, and intuitive, dedicated procedures. We demonstrate their efficiency in a real-world case study concerning the ranking of insulating materials based on the conflicting, incomplete preferences of a few tens of Decision Makers (DMs). For each DM, we consider a single representative ranking consistent with his/her preferences or thousands of such rankings following incorporation of the robustness concern. The experimental comparison is generalized to artificially generated problems that differ in terms of the numbers of alternatives and input rankings, and diversity levels. The results are quantified with the quality of obtained rankings and computation time. [ABSTRACT FROM AUTHOR]

Smart devices and technology applications are used in many fields. Much information is now recorded and collected rapidly so data analysis, especially clustering analysis, is vital to the process of analyzing and obtaining valuable information from datasets. However, data has different types of attributes: numerical, categorical, and mixed attributes. Some datasets also contain noise and outliers. An appropriate clustering is necessary to exploit the data structure. This study proposes a clustering algorithm that is called a possibilistic fuzzy k -modes (PFKM) algorithm. This combines the concept of possibility with the fuzzy k -modes (FKM) algorithm to address the effect of outliers and to improve the clustering results for categorical data. This study also implements three metaheuristics to increase clustering performance: a genetic algorithm (GA), a particle swarm optimization (PSO) and the sine-cosine algorithm (SCA). Three clustering algorithms are proposed: the GA-PFKM, PSO-PFKM, and SCA-PFKM algorithms. The performance of the algorithms is compared with that for the classical FKM algorithm using two indices: the sum-of-squared error (SSE) and the accuracy. The experimental results show that the PSO-PFKM and SCA-PFKM algorithms perform better for most datasets. [ABSTRACT FROM AUTHOR]

• ATO data is used to show the inefficiency of EEO strategy. • Data-driven method is proposed to simulate the regenerative energy absorption. • A uniformity measure is defined to quantify the impact of headway adjustment. • GA with adaptive operator selection and reduction technique is proposed. As an emerging energy-efficient management approach, the essential idea of subway regenerative energy optimization is to maximize the absorption of energy generated from train deceleration by adjusting train schedules. In the extant literature, performance evaluation and optimization rely on synthetic data (e.g., computer simulations) and train energy-efficient operation (EEO) strategy. However, when compared to real automatic train operation (ATO) data, the above-mentioned method exhibits significant errors. In this work, we develop a new optimization method driven by real ATO data for maximizing subway regenerative energy based on the following three steps: first, we provide a high-frequency ATO data-driven method for simulating the amount of regenerative energy absorption; second, we propose a concept of uniformity to measure the homogeneous degree of headway distribution; third, we formulate a headway optimization model to maximize the regenerative energy absorption under uniformity constraint. To handle the high complexity of the ATO data-driven objective function (e.g., non-monotonicity, non-convexity, multi-modality), we propose an improved genetic algorithm with multiple crossover and mutation operators to search for near-optimal solutions, in which an adaptive operator selection mechanism with reduction process on ATO data is considered for speeding up the regenerative energy simulation and optimization. The effectiveness of the proposed method is confirmed by using the real ATO data of Beijing Subway Changping line. Our numerical study reveals that, benchmarked with the uniform headway distribution (the policy that is presently in use), our proposed approach achieves a relative improvement of 7.75% at off-peak hours and 42.44% at peak hours for the regenerative energy absorption; and we show that such a significant performance improvement is obtained by allowing a small level of scheduling flexibility (less than 6% relaxation on uniformity level). [ABSTRACT FROM AUTHOR]