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Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach

DATA MINING AND KNOWLEDGE DISCOVERY 2024年第3期38卷 1289-1315页

作者： Moya, Antonio R. Veloso, Bruno Gama, Joao Ventura, Sebastian Univ Cordoba Andalusian Res Inst Data Sci & Computat Intelligen Dept Comp Sci & Numer Anal Cordoba Spain INESC TEC Porto Portugal Univ Porto FEP Porto Portugal

Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams.

关键词： Data streams Concept drift Optimisation Hyper-parameters evolutionary algorithms

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A novel evolutionary approach-based multimodal model to detect fake news in OSNs using text and metadata

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JOURNAL OF SUPERCOMPUTING 2024年第2期80卷 1522-1553页

作者： Uppada, Santosh Kumar Ashwin, B. S. Sivaselvan, B. IIITDM Kancheepuram Dept Comp Sci & Engn Chennai 600127 Tamil Nadu India

Social networks are the primary source of information sharing, and with the abundant availability of the internet, more and more information is being dumped into social networks from various sources. There is no proper credibility check, so social networks have become the main source for manipulated or inclined news. As information comes in multiple forms, there is a need for multimodal models to detect Fake News. Many features being specific to the domain may mislead the process of classifying news. Obtaining specific features that could retain the framework's capability becomes the point of interest. The paper proposes a novel method first to obtain features from a specific attribute and use a mechanism to derive optimal features that helps in attaining the ability to detect Fake News across domains. Genetic algorithms, termed nature-driven, aim at exploring the best optimal features that enhance the detection capability of the framework. Therefore, the paper proposes a model that works on textual features, metadata, and author embedding data and uses genetic algorithms to extract optimal features. These multimodal are combined using fusion methods, and finally, 2-Layer MLP is used to detect the Fake posts. Fakeddit, a multimodal dataset, is used for analysis. Text features are generated from the Title parameter, which is used as clickbait to attract attention. Genetic algorithms like two-phase gray wolf (TMGWO) and improved sine cosine algorithms (ISCA) are used to extract the best optimal features from the dataset. The proposed model has achieved an accuracy of 91.05% when ISCA is used to extract optimal features, BERT is used to analyze textual features, and metadata and author embedding are combined using Concatenate. The model has shown that extracting optimal features has enhanced the overall accuracy of the multimodal model. The proposed model is observed to outperform other state-of-the-art models working on similar data. To the best of our knowledge, the

关键词： Fake News Metadata evolutionary algorithms Fusion models Multilayer perceptron Two-phase gray wolf optimization Improved salp swarm algorithm Improved sine cosine algorithm Bare bones particle swarm optimization

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Surrogate Models and Ensemble Strategies for Expensive evolutionary Optimization: An Industrial Case Study

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IEEE ACCESS 2024年 12卷 86144-86159页

作者： Garza-Fabre, Mario Cortes-Garcia, Nicolas Galeana-Zapien, Hiram Landa, Ricardo Cinvestav Campus Tamaulipas Ctr Res & Adv Studies Ciudad Victoria 87130 Tamaulipas Mexico

Expensive optimization problems are characterized by the significant amount of time and resources needed to determine the quality of potential solutions. This poses severe limitations for the application of metaheuristic optimization methods, such as evolutionary algorithms, as they usually require evaluating many candidate solutions to deliver satisfactory results. Surrogate model-based strategies have become a popular choice to tackle this type of problem. The key idea of these strategies is to build a model which can approximate and (partially) replace the mechanisms for assessing solution quality, such that the use of this less expensive alternative lowers the overall computational cost of the optimization process. This paper analyzes surrogate model-based strategies in the context of a specific, expensive, combinatorial optimization problem: the configuration of the gas distribution system for an electrostatic precipitator. Focusing on this relevant case study from industry, the aim of this paper is twofold: (i) to investigate the most suitable learning techniques for building the surrogate models and (ii) to explore the advantages of ensemble strategies allowing various surrogate models to collaborate during optimization. This contrasts with previous studies where a single, fixed modeling technique is adopted to address this problem. The experimental evaluation involves eight different learning techniques, three alternative ensemble strategies, and two reference approaches from the literature (previously used to tackle this specific problem). Our results reveal the best modeling techniques at the individual level, while highlighting clear benefits of the simultaneous exploitation of multiple surrogate models when facing this particular optimization challenge.

关键词： Electrostatic precipitator problem evolutionary algorithms expensive optimization surrogate models surrogate model-based optimization surrogate-model ensembles

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PEAC-HNF: A Novel Multi-Objective evolutionary Algorithm for Split Delivery Vehicle Routing With Three-Dimensional Loading Constraints

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年

作者： Zhang, Han Li, Qing Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518000 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Lingnan Univ Sch Data Sci Hong Kong Peoples R China

The Split Delivery Vehicle Routing Problem with Three-Dimensional Loading Constraints (3L-SDVRP) integrates routing and packing problems, aiming to maximize the vehicle load efficiency and minimize the total travel distance. Solving 3L-SDVRP is critical for logistics and transportation industries. However, achieving an appropriate balance between exploration (searching for new solutions) and exploitation (refining known solutions) in metaheuristic algorithms for 3L-SDVRP, especially under limited computational resources, remains challenging. Furthermore, the application of multi-objective optimization algorithms to the 3L-SDVRP remains a largely unexplored area, particularly when considering the inherent trade-offs between the two conflicting objectives. To address these challenges, this paper introduces a new Pareto-based evolutionary Algorithm with Concurrent crossover and Hierarchical Neighborhood Filtering mutation (PEAC-HNF), distinguished by its novel Hierarchical Neighborhood Filtering (HNF) mutation. The HNF mutation uses diverse neighborhood structures to generate offspring, adopts a hierarchical strategy prioritizing individuals with higher nondomination ranks, and incorporates an offspring filtering process to save computational resources. HNF allows PEAC-HNF to improve its exploitation capabilities while maintaining exploration strengths, achieving a balanced performance. Comparisons with state-of-the-art algorithms across various problem instances (242 instances in total) demonstrate the effectiveness of PEAC-HNF. Further analysis highlights the critical role of the HNF mutation in enhancing algorithmic performance. The utilization of the HNF mutation can extend beyond PEAC-HNF to other complex optimization problems.

关键词： Loading Vehicle routing Three-dimensional displays Search problems Metaheuristics evolutionary computation Routing Mathematical models Indexes Complexity theory three-dimensional packing multi-objective optimisation metaheuristic algorithms evolutionary algorithms

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Cognitively Inspired Three-Way Decision Making and Bi-Level evolutionary Optimization for Mobile Cybersecurity Threats Detection: A Case Study on Android Malware

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COGNITIVE COMPUTATION 2024年第6期16卷 3200-3227页

作者： Jerbi, Manel Dagdia, Zaineb Chelly Bechikh, Slim Ben Said, Lamjed Univ Tunis ISG CS Dept SMART Lab Tunis Tunisia Univ Luxembourg Fac Sci Technol & Med L-4364 Esch Sur Alzette Luxembourg Univ Tunis LARODEC ISG Tunis Tunisia Univ Paris Saclay DAVID UVSQ Paris France

Malicious apps use a variety of methods to spread infections, take over computers and/or IoT devices, and steal sensitive data. Several detection techniques have been proposed to counter these attacks. Despite the promising results of recent malware detection strategies, particularly those addressing evolving threats, inefficiencies persist due to potential inconsistency in both the generated malicious malware and the pre-specified detection rules, as well as their crisp decision-making process. In this paper, we propose to address these issues by (i) considering the detection rules generation process as a Bi-Level Optimization Problem, where a competition between two levels (an upper level and a lower one) produces a set of effective detection rules capable of detecting new variants of existing and even unseen malware patterns. This bi-level strategy is subtly inspired by natural evolutionary processes, where organisms adapt and evolve through continuous interaction and competition within their environments. Furthermore, (ii) we leverage the fundamentals of Rough Set Theory, which reflects cognitive decision-making processes, to assess the true nature of artificially generated malicious patterns. This involves retaining only the consistent malicious patterns and detection rules and categorizing these rules into a three-way decision framework comprising accept, abstain, and reject options. Our novel malware detection technique outperforms several state-of-the-art methods on various Android malware datasets, accurately predicting new apps with a 96.76% accuracy rate. Moreover, our approach is versatile and effective in detecting patterns applicable to a variety of cybersecurity threats.

关键词： Android malware detection Bi-level optimization Rough set theory Artificial malicious patterns evolutionary algorithms Three-way decision making

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A Multi-Population Based evolutionary Algorithm for Many-Objective Recommendations

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1969-1982页

作者： Zhang, Lei Zhang, Huabin Chen, Zihao Liu, Sibo Yang, Haipeng Zhao, Hongke Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

Multi-objective evolutionary algorithms (MOEAs) have been proved to be competitive in recommender systems. As the application scenarios of recommender systems become increasingly complex, the number of objectives to be considered in the recommender systems increases. However, most existing multi-objective recommendation algorithms lead to increased environmental selection pressure as the number of objectives increases. To tackle the issue, in this paper, we propose a multi-population based evolutionary algorithm named MP-MORS for many-objective recommendations, where two subpopulations and one major population are used to evolve and interact to find high-quality solutions. Specifically, the objectives are firstly divided into those evaluated on individual users (defined as IndObjectives) and those evaluated on all users (defined as as AllObjectives). Then two subpopulations are suggested to optimize the two types of objectives respectively, with which the potential good solutions can be easily found. In addition, the major population considers the balance of all objectives and refines these potential good solutions. Finally, a set of high-quality solutions can be obtained by the proposed adaptive population interaction strategy. Experiments on the datasets Movielens and Douban show that the proposed MP-MORS outperforms the state-of-the-art algorithms for many-objective recommendations.

关键词： Recommender systems many-objective evolutionary algorithms multiple populations

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A Bilevel evolutionary Algorithm for Large-Scale Multiobjective Task Scheduling in Multiagile Earth Observation Satellite Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第6期54卷 3512-3524页

作者： Yao, Feng Chen, Yingguo Wang, Ling Chang, Zhongxiang Huang, Pei-Qiu Wang, Yong Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Changsha Univ Sci & Technol Sch Traff & Transport Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Natl Key Lab Green & Long Life Rd Engn Extreme Env Changsha 410114 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

This article studies a multiagile earth observation satellite system, in which a group of satellites provides observation services to acquire images of targets on the earth's surface. In this system, the large-scale multiobjective task scheduling problem is studied by jointly optimizing the task assignment scheme and the observation window allocation to maximize the total profit of all executed tasks on all satellites and the loading balance among satellites. Note, however, that it is challenging to solve this problem since the task assignment scheme and the observation window allocation are tightly coupled. Therefore, a bilevel optimization problem is formulated, where the tasks are assigned at the upper level and the observation windows are allocated at the lower level. In this way, the observation windows are allocated based on the given task assignment scheme, thus decoupling the task assignment scheme and the observation window allocation. Furthermore, the observation windows can be allocated in parallel on different satellites to improve computational efficiency. Subsequently, a bilevel evolutionary algorithm is proposed. Specifically, at the upper level, an initialization strategy is devised to efficiently generate feasible task assignment schemes by constructing the candidate satellite set for each task, and then a constrained multiobjective evolutionary algorithm is adopted to optimize the task assignment schemes. In addition, at the lower level, for each task assignment scheme, a greedy strategy is proposed to allocate the observation windows to as many tasks as possible on each satellite and a local search method is suggested to further improve the observation window allocation. Experiments on a diverse set of instances involving up to 1000 tasks demonstrate that the proposed algorithm exhibits better or at least competitive performance against other compared algorithms on each instance.

关键词： Bilevel optimization evolutionary algorithms multiagile earth observation satellite (AEOS) systems task scheduling

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Intelligent high-type control based on evolutionary multi-objective optimization

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MEASUREMENT & CONTROL 2024年第1期57卷 51-67页

作者： Zhang, Hanwen Liu, Qiong Mao, Yao Chinese Acad Sci Key Lab Opt Engn Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Key Lab Opt Engn Box 350 Chengdu 610209 Sichuan Peoples R China

In this paper, we formulate high-type intelligent control as a multi-objective problem and apply evolutionary algorithms to search for optimal solutions. Specifically, we consider the metrics of the system in both the frequency domain and the time domain. Integrated time and absolute error is used as a performance metric in the time domain, while bandwidth is used as a measure in the frequency domain. Simultaneously, the amplitude margin and phase margin are used as constraints to ensure the stability of the high-type control system. Then, we adopt evolutionary algorithms to solve the formulated multi-objective problem. Unlike most of the existing approaches, we formulate intelligent high type control as a multi-objective optimization problem based on our knowledge about the control system. Furthermore, evolutionary algorithms are adopted to search for optimal solutions to real-world controlling systems. Extensive experiments are conducted to evaluate the effectiveness of our proposed approach. Compared to the Z-N method and the extending symmetrical optimum criterion, our proposed method achieves an improvement in bandwidth of more than 126.6%, while reducing the overshoot by more than 56.8% and the settling time by more than 48.4% for all controlled objects used in the experiments. At the same time, the tracking errors of the ramp and parabolic signals are significantly reduced, which means this method effectively improves the system performance.

关键词： High-type control intelligent control multi-objective optimization evolutionary algorithms

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Autoencoder evolutionary algorithm for large-scale multi-objective optimization problem

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第11期15卷 5159-5172页

作者： Hu, Ziyu Xiao, Zhixing Sun, Hao Yang, He Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Tianjin Res Inst Elect Sci Tianjin 300180 Peoples R China

Multi-objective optimization problems characterized by a substantial number of decision variables, which are also called large-scale multi-objective optimization problems (LSMOPs), are becoming increasingly prevalent. Traditional evolutionary algorithms may deteriorate drastically when tackling a large number of decision variables. For LSMOPs, the dimensionality of the decision variables needs to be reduced and the algorithm needs to be designed according to the characteristics of divide-and-conquer. The autoencoder evolutionary algorithm (AEEA) is proposed based on autoencoder dimensionality reduction, the grouping of decision variables, and the application of divide-and-conquer strategies. The proposed algorithm is compared with other classical algorithms. The experiment result shows that AEEA achieves excellent convergence and diversity, and still performs well in decision variables of higher dimensions. Finally, it is verified that the autoencoder improves the running time of the proposed algorithm.

关键词： Large-scale Multi-objective optimization evolutionary algorithms Dimensionality reduction

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Random Testing and evolutionary Testing for Fuzzing GraphQL APIs

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ACM TRANSACTIONS ON THE WEB 2024年第1期18卷 1-41页

作者： Belhadi, Asma Zhang, Man Arcuri, Andrea Kristiania Univ Coll Prinsens Gate 7-9 N-0152 Oslo Norway Oslo Metropolitan Univ Prinsens Gate 7-9 N-0152 Oslo Norway

The Graph Query Language (GraphQL) is a powerful language for application programming interface (API) manipulation in web services. It has been recently introduced as an alternative solution for addressing the limitations of RESTful APIs. This article introduces an automated solution for GraphQL API testing. We present a full framework for automated API testing, from the schema extraction to test case generation. In addition, we consider two kinds of testing: white-box and black-box testing. The white-box testing is performed when the source code of the GraphQL API is available. Our approach is based on evolutionary search. Test cases are evolved to intelligently explore the solution space while maximizing code coverage and fault-finding criteria. The black-box testing does not require access to the source code of the GraphQL API. It is therefore of more general applicability, albeit it has worse performance. In this context, we use a random search to generate GraphQL data. The proposed framework is implemented and integrated into the open source EvoMaster tool. With enabled white-box heuristics (i.e., white-box mode), experiments on 7 open source GraphQL APIs and three search algorithms show statistically significant improvement of the evolutionary approach compared to the baseline random search. In addition, experiments on 31 online GraphQL APIs reveal the ability of the black-box mode to detect real faults.

关键词： GraphQL EvoMaster evolutionary algorithms automated testing random SBST SBSE fuzzing

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