Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.
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Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting correlated features has negative effect on classification performance. To tackle these problems, we present a novel multi-objective feature selection method that has several advantages. Firstly, it considers the interaction between features using an advanced probability scheme. Secondly, it is based on the Pareto Archived Evolution Strategy (PAES) method that has several advantages such as simplicity and its speed in exploring the solution space. However, we improve the structure of PAES in such a way that generates the offsprings, intelligently. Thus, the proposed method utilizes the introduced probability scheme to produce more promising offsprings. Finally, it is equipped with a novel strategy that guides it to find the optimum number of features through the process of evolution. The experimental results show a significant improvement in finding the optimal Pareto front compared to state-of-the-art methods on different real-world datasets.
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学习优化在接收器操作特性曲线(AUC)下的区域,近年来引起了不平衡数据的显着关注。虽然有几种AUC优化方法,但由于其成对学习风格,缩放UP OP优化仍然是一个开放问题。最大化大规模数据集中的AUC可以被视为非凸和昂贵的问题。灵感来自成对学习的特征,构建了具有从大型数据集采样的小型数据集的廉价AUC优化任务,以促进原始,大规模和昂贵的AUC优化任务的AUC准确性。本文开发了一种进化的多任务框架(称为EMTAUC),以充分利用构造的便宜和昂贵的任务之间的信息,以获得更高的性能。在EMTAUC中,一个任务是优化来自采样数据集的AUC,另一个任务是从原始数据集中最大化AUC。此外,由于包含有限知识的廉价任务,提出了一种动态调整廉价任务数据结构的策略,以引入更多知识,进入多任务定量的AUC优化环境。在一系列二进制分类数据集上评估所提出的方法的性能。实验结果表明,EMTAUC对单一任务方法和在线方法具有竞争力。可以在HTTPS://github.com/xiaofangxd/emtauc访问EMTAUC的补充材料和源代码。
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信息科学的快速发展引起的“维度诅咒”在处理大数据集时可能会产生负面影响。在本文中,我们提出了Sparrow搜索算法(SSA)的一种变体,称为帐篷L \'evy飞行麻雀搜索算法(TFSSA),并使用它来选择包装模式中最佳的特征子集以进行分类。 SSA是最近提出的算法,尚未系统地应用于特征选择问题。通过CEC2020基准函数进行验证后,TFSSA用于选择最佳功能组合,以最大化分类精度并最大程度地减少所选功能的数量。将拟议的TFSSA与文献中的九种算法进行了比较。 9个评估指标用于正确评估和比较UCI存储库中21个数据集上这些算法的性能。此外,该方法应用于冠状病毒病(COVID-19)数据集,分别获得最佳的平均分类精度和特征选择的平均数量,为93.47%和2.1。实验结果证实了所提出的算法在提高分类准确性和减少与其他基于包装器的算法相比的选定特征数量方面的优势。
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影响最大化是挖掘社交网络深入信息的关键问题,该信息旨在选择从网络中选择种子以最大程度地增加受影响的节点的数量。为了评估种子套装的影响,现有的努力提出了拟议的代理模型(转换),以较低的计算成本来代替昂贵的蒙特卡洛模拟过程。这些基于网络先验知识的替代转换从各个角度引起具有相似特征的不同搜索行为。对于特定情况,用户很难先验确定合适的转换。在本文中,我们提出了一个多种转化的进化框架,以进行影响最大化(MTEFIM),并保证了融合保证,以利用替代转换的潜在相似性和独特的优势,并避免用户手动确定最合适的转换。在MTEFIM中,将多个转换同时优化为多个任务。每个转换均分配一个进化求解器。进行了MTEFIM的三个主要组成部分:1)根据不同人群的个人(种子集)重叠程度估算转化之间的潜在关系,2)根据转变关系,将个体转移到跨种群中,3)选择最终输出种子集,包含所有代理模型知识。 MTEFIM的有效性在基准和现实世界社交网络上得到了验证。实验结果表明,与几种流行的IM特异性方法相比,MTEFIM可以有效地利用跨多个转换的潜在转移知识,以实现高度竞争性能。可以在https://github.com/xiaofangxd/mtefim上访问MTEFIM的实现。
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Metaheuristics are popularly used in various fields, and they have attracted much attention in the scientific and industrial communities. In recent years, the number of new metaheuristic names has been continuously growing. Generally, the inventors attribute the novelties of these new algorithms to inspirations from either biology, human behaviors, physics, or other phenomena. In addition, these new algorithms, compared against basic versions of other metaheuristics using classical benchmark problems without shift/rotation, show competitive performances. In this study, we exhaustively tabulate more than 500 metaheuristics. To comparatively evaluate the performance of the recent competitive variants and newly proposed metaheuristics, 11 newly proposed metaheuristics and 4 variants of established metaheuristics are comprehensively compared on the CEC2017 benchmark suite. In addition, whether these algorithms have a search bias to the center of the search space is investigated. The results show that the performance of the newly proposed EBCM (effective butterfly optimizer with covariance matrix adaptation) algorithm performs comparably to the 4 well performing variants of the established metaheuristics and possesses similar properties and behaviors, such as convergence, diversity, exploration and exploitation trade-offs, in many aspects. The performance of all 15 of the algorithms is likely to deteriorate due to certain transformations, while the 4 state-of-the-art metaheuristics are less affected by transformations such as the shifting of the global optimal point away from the center of the search space. It should be noted that, except EBCM, the other 10 new algorithms proposed mostly during 2019-2020 are inferior to the well performing 2017 variants of differential evolution and evolution strategy in terms of convergence speed and global search ability on CEC 2017 functions.
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多模式的多目标问题(MMOP)通常在现实世界中出现,而决策空间中遥远的解决方案对应于非常相似的目标值。为了获得MMOP的所有溶液,已经提出了许多多模式多模式的多模型进化算法(MMEAS)。目前,很少有研究涵盖了最近提出的大多数代表性MMEAS,并进行了比较。在这项研究中,我们首先回顾了过去二十年中相关作品。然后,我们选择了12种利用不同多样性维护技术的最先进的算法,并比较了它们在现有测试套件上的性能。实验结果表明,不同类型的MMOP上不同技术的优势和劣势,从而为如何在特定情况下选择/设计MMEAS提供指导。
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最近几十年来,已经采用了用于解决各种多主体优化问题(MOPS)的多主体进化算法(MOEAS)的显着进步。但是,这些逐渐改善的MOEAS并不一定配备了精致的可扩展和可学习的解决问题的策略,这些策略能够应对缩放型拖把带来的新的和宏伟的挑战,并不断提高各种方面的复杂性或规模,主要包括昂贵的方面,包括昂贵的方面。功能评估,许多目标,大规模搜索空间,时变环境和多任务。在不同的情况下,它需要不同的思考来设计新的强大MOEAS,以有效地解决它们。在这种情况下,对可学习的MOEAS进行的研究,以机器学习技术进行缩放的拖把,在进化计算领域受到了广泛的关注。在本文中,我们从可扩展的拖把和可学习的MOEAS的分类学开始,然后分析将拖把构成对传统MOEAS的挑战的分析。然后,我们综合概述了可学习的MOEAS的最新进展,以求解各种扩展拖把,主要集中在三个有吸引力的有前途的方向上(即,可学习的环境选择的可学习的进化鉴别器,可学习的进化生物的可学习生殖发生器,以及可学习的进化转移,用于分享或分享或分享或进行分享或可学习的转移。不同问题域之间的经验)。在本文中提供了有关可学习的MOEAS的见解,以参考该领域的努力的一般踪迹。
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计算机视觉(CV)是涵盖广泛应用的人工智能中的一个重要领域。图像分析是CV的主要任务,目的是提取,分析和理解图像的视觉内容。但是,由于许多因素,图像之间的较高变化,高维度,域专业知识要求和图像扭曲,因此与图像相关的任务非常具有挑战性。进化计算方法(EC)方法已被广泛用于图像分析,并取得了重大成就。但是,没有对现有的EC方法进行图像分析的全面调查。为了填补这一空白,本文提供了一项全面的调查,涵盖了重要的图像分析任务的所有基本EC方法,包括边缘检测,图像分割,图像特征分析,图像分类,对象检测等。这项调查旨在通过讨论不同方法的贡献并探讨如何以及为什么将EC用于简历和图像分析,以更好地了解进化计算机视觉(ECV)。还讨论并总结了与该研究领域相关的应用,挑战,问题和趋势,以提供进一步的指南和未来研究的机会。
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排名汇总旨在将许多替代品的偏好排名与不同选民的偏替排名组合成单一共识排名。然而,作为各种实际应用的有用模型,它是一个计算上有挑战性的问题。在本文中,我们提出了一种有效的混合进化排名算法来解决完整和部分排名的排名聚集问题。该算法具有基于协调对的语义交叉,并通过有效的增量评估技术加强了较晚的验收本地搜索。进行实验以评估算法,与最先进的算法相比,表明基准实例上具有高度竞争性能。为了展示其实际有用性,算法应用于标签排名,这是一个重要的机器学习任务。
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近年来,行业和学术界的深度学习(DL)迅速发展。但是,找到DL模型的最佳超参数通常需要高计算成本和人类专业知识。为了减轻上述问题,进化计算(EC)作为一种强大的启发式搜索方法显示出在DL模型的自动设计中,所谓的进化深度学习(EDL)具有重要优势。本文旨在从自动化机器学习(AUTOML)的角度分析EDL。具体来说,我们首先从机器学习和EC阐明EDL,并将EDL视为优化问题。根据DL管道的说法,我们系统地介绍了EDL方法,从功能工程,模型生成到具有新的分类法的模型部署(即,什么以及如何发展/优化),专注于解决方案表示和搜索范式的讨论通过EC处理优化问题。最后,提出了关键的应用程序,开放问题以及可能有希望的未来研究线。这项调查回顾了EDL的最新发展,并为EDL的开发提供了有见地的指南。
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Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. Evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 7 evolving registration approaches and 4 traditional registration approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.
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二进制矩阵优化通常是在现实世界中出现的,例如多微晶网络结构设计问题(MGNSDP),即在某些约束下最小化电源线的总长度。为这些问题找到全球最佳解决方案面临着一个巨大的挑战,因为此类问题可能是大规模,稀疏和多模式。传统的线性编程是耗时的,无法解决非线性问题。为了解决这个问题,提出了一种新颖的可行性规则基于差异进化算法,称为LBMDE。具体来说,首先提出了一种通用启发式溶液初始化方法来生成高质量的解决方案。然后,引入了基于二进制的DE操作员以生产后代。为了处理约束,我们提出了改进的基于可行性规则的环境选择策略。通过一组基准问题来检查LBMDE的性能和搜索行为。
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Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
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本文侧重于从非线性和复杂动态系统的动态推断网络结构和社区结构,在许多领域突出。已经提出了许多方法来完全解决这两个问题,但他们都不考虑这两个任务中的明确可共同的知识。灵感来自于,更精确的网络结构可以促进社区发现的准确性和更好的社区可以促进网络重建的性能(NR),这篇论文开发了一种进化的多任务框架,以充分利用这两个中的明确可共同的知识提高他们的表现的任务;我们将此框架称为EMTNRCD。在EMTNRCD中,我们首先将这两个任务建立为一个多任务NR和社区检测(CD)问题,其中一个任务是从动态重建网络结构,另一个任务是从动态发现社区。在EMTNRCD的过程中,NR任务明确地传输了CD任务的几个更好的网络结构,CD任务明确地传输了更好的社区结构,以帮助NR任务,这提高了NR任务的重建准确性和社区划分质量CD任务。此外,要将知识从NR任务的研究转移到CD任务,EMTNRCD模型从动态的CD研究作为查找动态网络中的社区问题,然后决定是否在任务中进行知识转移。本文还为多任务NR和CD问题(MTNRCDP)设计了一个测试套件,以验证EMTNRCD的性能。实验结果表明,带Cd的关节NR具有协同效应。
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多目标优化问题的目标在现实世界中通常会看到不同的评估成本。现在,此类问题被称为异质目标(HE-MOPS)的多目标优化问题。然而,到目前为止,只有少数研究来解决HE-MOPS,其中大多数专注于一个快速目标和一个缓慢目标的双向目标问题。在这项工作中,我们旨在应对具有两个以上黑盒和异质目标的He-mops。为此,我们通过利用He-Mops中廉价且昂贵的目标的不同数据集来减轻因评估不同目标而导致的搜索偏见,从而减轻了廉价且昂贵的目标,从而为HE-MOPS开发了多目标贝叶斯进化优化方法。为了充分利用两个不同的培训数据集,一种对所有目标进行评估的解决方案,另一个与仅在快速目标上进行评估的解决方案,构建了两个单独的高斯过程模型。此外,提出了一种新的采集函数,以减轻对快速目标的搜索偏见,从而在收敛与多样性之间达到平衡。我们通过对广泛使用的多/多目标基准问题进行测试来证明该算法的有效性,这些问题被认为是异质昂贵的。
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Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.
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肺炎是儿童死亡率的主要原因之一,尤其是在全球收入的地区。尽管可以通过不太复杂的仪器和药物进行检测和治疗,但肺炎检测仍然是发展中国家的主要关注点。基于计算机辅助的诊断(CAD)系统可在此类国家 /地区使用,因为其运营成本低于专业医疗专家。在本文中,我们使用深度学习的概念和一种元神父算法提出了一个从胸部X射线检测的CAD系统,以检测胸部X射线。我们首先从预先训练的RESNET50中提取深度功能,该功能在目标肺炎数据集上进行了微调。然后,我们提出了一种基于粒子群优化(PSO)的特征选择技术,该技术使用基于内存的适应参数进行了修改,并通过将利他行为纳入代理人而丰富。我们将功能选择方法命名为自适应和利他的PSO(AAPSO)。提出的方法成功地消除了从RESNET50模型获得的非信息性特征,从而提高了整体框架的肺炎检测能力。对公开可用的肺炎数据集进行了广泛的实验和彻底分析,确定了所提出的方法比用于肺炎检测的其他几个框架的优越性。除了肺炎检测外,AAPSO还可以在某些标准的UCI数据集,用于癌症预测的基因表达数据集和COVID-19预测数据集上进行评估。总体结果令人满意,从而确认AAPSO在处理各种现实生活问题方面的实用性。可以在https://github.com/rishavpramanik/aapso上找到此工作的支持源代码
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传感器节点(SNS)的部署总是在无线传感器网络(WSN)的系统性能中起决定性作用。在这项工作中,我们提出了一种实用异构WSN的最佳部署方法,该方法可以深入了解可靠性和部署成本之间的权衡。具体而言,这项工作旨在提供SNS的最佳部署,以最大程度地提高覆盖率和连接学位,同时最大程度地减少整体部署成本。此外,这项工作充分考虑了SNS的异质性(即差异化的传感范围和部署成本)和三维(3-D)部署方案。这是一个多目标优化问题,非凸,多模态和NP-HARD。为了解决它,我们开发了一种新型的基于群体的多目标优化算法,称为竞争性多目标海洋掠食者算法(CMOMPA),其性能通过与十种其他多个多目标优化的全面比较实验验证算法。计算结果表明,在收敛性和准确性方面,CMOMPA优于他人,并且在多模式多目标优化问题上表现出卓越的性能。还进行了足够的模拟来评估基于CMOMPA的最佳SNS部署方法的有效性。结果表明,优化的部署可以平衡部署成本,感知可靠性和网络可靠性之间的权衡平衡。源代码可在https://github.com/inet-wzu/cmompa上找到。
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由于问题的大规模性质,机器学习算法中的封锁率调整是一种计算挑战性的任务。为了开发高参数调整的有效策略,一个有希望的解决方案是使用群体智能算法。人造蜜蜂殖民地(ABC)优化为此目的作为一个有希望有效的优化算法。然而,在某些情况下,由于初始解决方案较差和昂贵的客观函数,ABC可能遭受缓慢的收敛速度或执行时间。为了解决这些问题,提出了一种新颖的算法,OPTABC,以帮助ABC算法更快地达到近最佳解决方案。 Optabc集成了人造蜂殖民地算法,K均值聚类,贪婪算法和基于反对的学习策略,用于调整不同机器学习模型的超参数。 Optabc采用这些技术,以试图多样化初始群体,因此增强了收敛能力,而不会显着降低准确性。为了验证所提出的方法的性能,我们将结果与先前的最先进的方法进行比较。实验结果表明,与文献中的现有方法相比,Optabc的有效性。
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