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An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Computers, Materials & Continua 2023年第2期74卷 4119-4133页

作者： Hasanien K.Kuba Mustafa A.Azzawi Saad M.Darwish Oday A.Hassen Ansam A.Abdulhussein University of Information Technology and Communication(UOITC) Baghdad10081Iraq Accenture Ling Karan TRXKuala Lumpur55188Malaysia Department of Information Technology Institute of Graduate Studies and ResearchUniversity of Alexandria21526Egypt Ministry of Education Wasit Education DirectorateKut52001Iraq Information Technology Center Iraqi Commission for Computers and InformaticsBaghdad10081Iraq

It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible *** from several sources must be combined for use in many data mining *** medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive ***,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive *** is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to *** this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’*** applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining *** study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization *** search is advantageous since it removes a huge number of unnecessary rules and item *** using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.

关键词： Distributed data mining evolutionary computation sanitization process healthcare informatics

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MPF-FS: A multi-population framework based on multi-objective optimization algorithms for feature selection

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APPLIED INTELLIGENCE 2023年第19期53卷 22179-22199页

作者： Yang, Jie He, Junjiang Li, Wenshan Li, Tao Lan, Xiaolong Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Sch Cyber Sci & Engn 24 Sect 1Xuefu RdSouthwest Airport Econ Dev Zone Chengdu 610225 Peoples R China

Feature selection algorithms based on evolutionary computation have continued to emerge, and most of them have achieved outstanding results. However, there are two drawbacks when facing high-dimensional datasets: firstly, it is difficult to reduce features effectively, and secondly, the "curse of dimensionality". To alleviate those problems, we take the initial population generation as an entry point and propose a variant initial population generator, which can improve diversity and initialize populations randomly throughout the solution space. However, during the experimental process, it was found that the improved diversity would cause the algorithm to converge too fast and thus lead to premature. Therefore, we introduced multi-population techniques to balance diversity and convergence speed, and finally formed the MPF-FS framework. To prove the effectiveness of this framework, two feature selection algorithms, multi-population multi-objective artificial bee colony algorithm and multi-population non-dominated sorting genetic algorithm II, are implemented based on this framework. Nine well-known public datasets were used in this study, and the results reveal that the two proposed multi-population methods on high-dimensional datasets can reduce more features without reducing (or even improving) classification accuracy, which outperforms the corresponding single-population algorithms. Further compared to the state-of-the-art methods, our method still shows promising results.

关键词： Feature selection evolutionary computation Multi-objective optimization Genetic algorithm Artificial bee colony algorithm

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Crowd Management Through Optimal Layout of Fences: An Ant Colony Approach Based on Crowd Simulation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9137-9149页

作者： Liao, Xiao-Cheng Chen, Wei-Neng Guo, Xiao-Qi Zhong, Jinghui Hu, Xiao-Min South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou 510006 Peoples R China

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences to guide crowd movement has become a widely adopted approach to alleviate congestion. This work presents a method that combines crowd simulation and management, focusing on the optimization of the fence layout for efficient crowd guidance. First, a congestion probability social force model (CP-SFM) is introduced to simulate the irrational pedestrians and to evaluate the efficacy of different fence layouts. Second, based on CP-SFM, we are the first to formulate the fence layout problem as an optimization problem with the objective to minimize the congestion of pedestrians in public places. Third, we further propose an ant colony crowd intervention algorithm (ACCI) to optimize the layout of fences. Lastly, we illustrate the performance of proposed ACCI on 18 scenarios including two real-world subway stations. Compared with other optimization methods, ACCI demonstrates promising performance in avoiding crowd congestion.

关键词： Layout Force Behavioral sciences Optimization Public transportation Navigation Safety Ant colony system ant colony optimization crowd management layout optimization evolutionary computation

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evolutionary Learning of Interpretable Decision Trees

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IEEE ACCESS 2023年 11卷 6169-6184页

作者： Custode, Leonardo L. Iacca, Giovanni Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

In the last decade, reinforcement learning (RL) has been used to solve several tasks with human-level performance. However, there is a growing demand for interpretable RL, i.e., there is the need to understand how a RL agent works and the rationale of its decisions. Not only do we need interpretability to assess the safety of such agents, but also we may need it to gain insights into unknown problems. In this work, we propose a novel optimization approach to interpretable RL that builds decision trees. While techniques that optimize decision trees for RL do exist, they usually employ greedy algorithms or do not exploit the rewards given by the environment. For these reasons, these techniques may either get stuck in local optima or be inefficient. On the contrary, our approach is based on a two-level optimization scheme that combines the advantages of evolutionary algorithms with the benefits of Q -learning. This method allows decomposing the problem into two sub-problems: the problem of finding a meaningful decomposition of the state space, and the problem of associating an action to each subspace. We test the proposed method on three well-known RL benchmarks, as well as on a pandemic control task, on which it results competitive with the state-of-the-art in both performance and interpretability.

关键词： Reinforcement learning Optimization Decision trees Behavioral sciences Pandemics evolutionary computation Q-learning Decision tree evolutionary algorithm interpretability reinforcement learning

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Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization Problems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2023年第3期7卷 684-699页

作者： Wang, Zi-Jia Zhan, Zhi-Hui Li, Yun Kwong, Sam Jeon, Sang-Woon Zhang, Jun Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China i4AI Ltd London WCIN 3AX England City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Ansan 15588 South Korea

Local search has been regarded as a promising technique in multimodal algorithms to refine the accuracy of found multiple optima. However, how to execute the local search operations precisely on the found global optima and avoid the meaningless local search operations on local optima or found similar areas is still a challenge. In this paper, we propose a novel local search technique based on the individual information from two aspects, termed as fitness and distance based local search (FDLS). The fitness information can avoid the ineffective local search operations on the local optima, while the distance information can avoid the meaningless local search operations on the similar areas. These two kinds of information act in different roles and complement each other, which ensures that the local search is executed in different (ensured by distance information) and promising (ensured by fitness information) areas, leading to successful local search. Based on this, we design an adaptive DE (ADE) with adaptive parameters scheme and apply FDLS to ADE, termed as FDLS-ADE. Experimental results on the CEC2015 multimodal competition show the effectiveness and superiority of the FDLS-ADE, including comparisons with the winner of the CEC2015 multimodal competition. Furthermore, compared with other multimodal algorithms, the performance of the FDLS-ADE is seen relatively insensitive to niching parameters. Besides, experiments conducted also show that the FDLS can be applied to other multimodal algorithms easily and can further improve their performance. Finally, an application to a real-world nonlinear equations system further illustrates the applicability of the FDLS-ADE.

关键词： Iron Search problems Sociology Optimization Clustering algorithms Transforms Sensitivity Fitness and distance local search differential evolution multimodal optimization problems evolutionary computation

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Strength Learning Particle Swarm Optimization for Multiobjective Multirobot Task Scheduling

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第7期53卷 4052-4063页

作者： Liu, Xiao-Fang Fang, Yongchun Zhan, Zhi-Hui Zhang, Jun Nankai Univ Inst Robot & Automatic Informat Syst Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Ansan 15588 South Korea

Cooperative heterogeneous multirobot systems have attracted increasing attention in recent years. They use multiple heterogeneous robots to execute complex tasks in a coordinated way. The allocation of heterogeneous robots to cooperative tasks is a significant and challenging optimization problem. However, little work has gone into scheduling large-scale cooperative tasks with precedence constraints and multiple conflicting optimization objectives. Existing methods are insufficient to address the issue. We propose a multiobjective model and develop strength learning particle swarm optimization (SLPSO) to optimize multiple objectives. In this article, the problem is converted into a two-step problem of task permutation construction and robot subset selection. In order to coordinate with the time-extended property of the problem, SLPSO utilizes a hybrid encode scheme: an element-based representation for task permutations and a binary representation for robot coalitions. A strength learning strategy with heuristic information guides particles to enhance their best-performing objectives for improving swarm convergence. In addition, an estimation-based local search is developed to improve spare solutions for enhancing swarm diversity, which determines the search direction by estimating fitness improvements. Experimental results on thirty problem instances are elaborated to demonstrate that the proposed SLPSO significantly outperforms the state-of-the-art algorithms in terms of inverted generational distance and hypervolume metrics. The proposed SLPSO can obtain a set of high-quality and diversified solutions.

关键词： Task analysis Robots Robot kinematics Multi-robot systems Search problems Particle swarm optimization Metaheuristics Cooperative evolutionary computation multiobjective multirobot systems multirobot task scheduling particle swarm optimization (PSO)

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Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

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IEEE ACCESS 2023年 11卷 143069-143080页

作者： Pietrosanti, Giulia Marchiori Nadizar, Giorgia Pigozzi, Federico Medvet, Eric Univ Trieste Dept Math & Geosci Trieste I-34127 Italy Univ Trieste Dept Engn & Architecture Trieste I-34127 Italy

Robots with a modular body permit a wide range of physical configurations, which can be obtained by arranging the composing modules differently. While this freedom makes modular robots capable of performing different tasks, finding the optimal physical configuration for a given task is not trivial. In fact, practitioners attempt to jointly optimize the body and the controller of the robot for a given task, but the result is not always satisfactory. More broadly, it is not clear what factors make a physical configuration more or less successful. In this paper, we aim to fill this gap and verify if humans can be predictive with respect to the performance of an optimized controller for a given robot body. We consider the case of Voxel-based Soft Robots (VSRs), whose rich dynamic induced by the softness of the modules makes the body particularly relevant for the robot ability to perform a task. We instantiate a number of (simulated) VSR bodies, differing in shape and actuation mechanism, and let a panel of humans control them, by means of online interaction with the simulator, while performing the task of locomotion. We use the same bodies with controllers obtained with evolutionary optimization, for the same task. We compare the ranking of human- and optimized AI-based VSRs, finding them very similar. We believe that our results strengthen the hypothesis that intrinsic factors in the body of modular robots determine their success.

关键词： Control voxel-based soft robots evolutionary computation

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A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

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SOFTWARE AND SYSTEMS MODELING 2023年第4期22卷 1281-1318页

作者： John, Stefan Kosiol, Jens Lambers, Leen Taentzer, Gabriele Philipps Univ Marburg Marburg Germany Brandenburg Tech Univ Cottbus Senftenberg Cottbus Germany

Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.

关键词： Search-Based Software Engineering Model-Driven Engineering evolutionary computation

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Hybrid genetic algorithms for the determination of DNA motifs to satisfy postulate 2-Optimality

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APPLIED INTELLIGENCE 2023年第8期53卷 8644-8653页

作者： Dai Tho Dang Ngoc Thanh Nguyen Hwang, Dosam Univ Danang Vietnam Korea Univ Informat & Commun Technol Danang Vietnam Wroclaw Univ Sci & Technol Fac Informat & Commun Technol Dept Appl Informat Wroclaw Poland Nguyen Tat Thanh Univ Fac Informat Technol Nguyen Tat Thanh Vietnam Yeungnam Univ Dept Comp Engn Yeungnam South Korea

Currently, determining DNA motifs or consensus plays an indispensable role in bioinformatics. Many postulates have been proposed for finding a consensus. Postulate 2-Optimality is essential for this task. A consensus satisfying postulate 2-Optimality is the best representative of a profile, and its distances to the profile members are uniform. However, this postulate has not been widely investigated in identifying a DNA motif or consensus for a DNA motif profile. The HDC algorithm is the best at this task in the literature. This study focuses on determining DNA motifs that satisfy postulate 2-Optimality. We propose a new hybrid genetic (HG1) algorithm based on the elitism strategy and local search. Subsequently, a novel elitism strategy and longest distance strategy are introduced to maintain the balance of exploration and exploitation. A new hybrid genetic (HG2) algorithm is developed based on the proposed exploration and exploitation balance approach. The simulation results show that these algorithms provide a high-quality DNA motif. The HG2 algorithm provides a DNA motif with the best quality.

关键词： evolutionary computation Hybrid genetic algorithm Postulate 2-Optimality DNA motif DNA sequence

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Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3873-3886页

作者： Liang, Jing Qiao, Kangjia Yu, Kunjie Qu, Boyang Yue, Caitong Guo, Weifeng Wang, Ling Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be optimized and various constraints to be satisfied, which challenges the evolutionary algorithms in balancing the objectives and constraints. This article attempts to explore and utilize the relationship between constrained Pareto front (CPF) and unconstrained Pareto front (UPF) to solve CMOPs. Especially, for a given CMOP, the evolutionary process is divided into the learning stage and the evolving stage. The purpose of the learning stage is to measure the relationship between CPF and UPF. To this end, we first create two populations and evolve them by specific learning strategies to approach the CPF and UPF, respectively. Then, the feasibility information and dominance relationship of the two populations are used to determine the relationship. Based on the learned relationship, specific evolving strategies are designed in the evolving stage to improve the utilization efficiency of objective information, so as to better solve this CMOP. By the above process, a new constrained multiobjective evolutionary algorithm (CMOEA) is presented. Comprehensive experimental results on 65 benchmark functions and ten real-world CMOPs show that the proposed method has a better or very competitive performance in comparison with several state-of-the-art CMOEAs. Moreover, this article demonstrates that using the relationship between CPF and UPF to guide the utilization of objective information is promising in solving CMOPs.

关键词： Statistics Sociology Optimization Linear programming Search problems evolutionary computation Shape Constrained multiobjective optimization evolutionary algorithm objective function relationship

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