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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： optimization algorithms

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COMPUTER-SYSTEM DESIGN USING A HIERARCHICAL APPROACH TO PERFORMANCE EVALUATION

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COMMUNICATIONS OF THE ACM 1980年第9期23卷 511-521页

作者： KUMAR, B DAVIDSON, ES UNIV ILLINOIS COORDINATED SCI LABURBANAIL 61801

The concept of a hierarchy of performance models is introduced. It is argued that such a hierarchy should consist of models spanning a wide range of accuracy and cost in order to be a cost-effective tool in the design of computer systems. Judicious use of the hierarchy can satisfy the conflicting needs of high accuracy and low cost of performance evaluation. A system design procedure that uses the hierarchy is developed. The concepts developed are illustrated by applying them to a case study of system design. The results of optimizations conducted using a two-level performance model hierarchy and a simple cost model are discussed. In almost all the experiments conducted, the optimization procedure converged to a region very close to a locally optimum system. The efficiency of the procedure is shown to be considerably greater than that of the brute force approach to system design. [ABSTRACT FROM AUTHOR]

关键词： hierarchical modeling high speed computer systems optimization algorithms performance evaluation system design

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A Newton Method for Hausdorff Approximations of the Pareto Front Within Multi-objective Evolutionary algorithms

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IEEE Transactions on Evolutionary Computation 2024年

作者： Wang, Hao Rodriguez-Fernandez, Angel E. Uribe, Lourdes Deutz, Andre Cortes-Pina, Oziel Schutze, Oliver Leiden Institute of Advanced Computer Science of Leiden University Leiden Netherlands Applied Quantum Algorithms Group Leiden University Netherlands Computer Science Department of the CINVESTAV Mexico City Mexico Faculty of Actuarial Sciences of Universidad Anáhuac México Mexico IPN Mexico City Mexico

A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly optimal approximations. Hereby, optimality is implicitly defined by the chosen performance indicator In this work, we propose a set-based Newton method for Hausdorff approximations of the Pareto front to be used within multi-objective evolutionary algorithms. To this end, we first generalize the previously proposed Newton step for the performance indicator to treat constrained problems for general reference sets. To approximate the target Pareto front, we propose a particular strategy for generating the reference set that utilizes the data gathered by the evolutionary algorithm during its run. Finally, we show the benefit of the Newton method as a post-processing step on several benchmark test functions and different base evolutionary algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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Air quality prediction by neuro-fuzzy modeling approach

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Lin, Yu-Chun Lee, Shie-Jue Ouyang, Chen-Sen Wu, Chih-Hung Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Intelligent Elect Commerce Res Ctr Kaohsiung Taiwan I Shou Univ Dept Informat Engn Kaohsiung Taiwan Natl Univ Kaohsiung Dept Elect Engn Kaohsiung Taiwan

This paper proposes an air quality prediction system based on the neuro-fuzzy network approach. Historical time series data are employed to derive a set of fuzzy rules, or equivalently a neuro-fuzzy network, for forecasting air pollutant concentrations and environmental factors in the future. Due to the uncertainty of the involved impact factors, fuzzy elements are added to the forecasting system. First of all, training data are partitioned into fuzzy clusters whose membership functions are characterized by the estimated means and variances. From these fuzzy clusters, fuzzy rules are extracted and a four-layer fuzzy neural network is constructed. Then genetic, particle swarm optimization, and steepest descent backpropagation algorithms are applied to train the network. The network outputs, derived through the fuzzy inference process, produce the forecast air pollutant concentrations or air quality indices. Our proposed approach has the following advantages: (1) Adding fuzzy elements can more appropriately deal with the uncertainty of the impact factors involved;(2) The distribution of training data can be described properly by fuzzy clusters with statistical means and variances;(3) Fuzzy rules are extracted automatically from the training data, instead of being supplied manually by human experts;(4) The obtained fuzzy rules are of high quality, and their parameters can be optimized effectively. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Air quality Fuzzy rules Fuzzy neural network Time series Clustering optimization algorithms

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Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

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GENETICS 2009年第3期181卷 1101-1113页

作者： Yi, Nengjun Banerjee, Samprit Univ Alabama Birmingham Dept Biostat Sect Stat Genet Birmingham AL 35294 USA Cornell Univ Weill Med Coll Dept Publ Hlth Div Biostat & Epidemiol New York NY 10021 USA

We develop hierarchical generalized linear models and computationally efficient algorithms for genomewide analysis of quantitative trait loci (QTL) for various types of phenotypes ill experimental crosses. The proposed models can fit it large number of effects, including covariates, main effects of numerous loci, and gene-gene (epistasis) and gene-environment. (G X E) interactions. The key to the approach is the use of continuous prior distribution oil coefficients that favors sparseness ill the filled model and facilitates computation. We develop it fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares For classical generalized linear models as implemented ill the package R. We propose a model search strategy, to build a parsimonious model. Our method takes advantage of the special correlation structure in QTL (lata. Simulation studies demonstrate reasonable power to detect true effects, while controlling the rate of false positives. We illustrate with three real data sets and compare our method to existing methods for multilple-QTL. mapping. Our method has been implemented in our freely available package R/qtlbim (***), providing a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach.

关键词： Software Studies Genomics Generalized linear models Variables Research methodology optimization algorithms Methods Gene expression Advantages Data analysis

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College English Teaching Quality Evaluation Method Based on Differential Evolution Algorithm

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International Journal of High Speed Electronics and Systems 2024年第0期

作者： Zhang, Suojun Jiao, Haiqi School of Foreign Languages Quzhou University Quzhou324000 China China Mobile Group Inner Mongolia Co. Ltd. Hulunbuir Branch Hulunbuir021008 China

This study proposes a college English teaching quality evaluation method based on the differential evolution (DE) algorithm. Traditional evaluation methods are often subjective, lacking objectivity and relying on qualitative analysis, which can be insufficient for comprehensive and systematic assessments. In this context, the DE algorithm is integrated into the evaluation system to adaptively adjust the weights of evaluation indicators, thereby better reflecting the key factors of teaching quality. The efficacy of the proposed method is demonstrated through experiments conducted on real-world college English courses. The results suggest that, compared to traditional methods, the DE-based evaluation is more objective, comprehensive, and precise, offering tailored recommendations for improvement. This novel evaluation method provides a fresh perspective for enhancing teaching quality and offers a robust decision-making foundation for administrators, instructors, and policymakers. The key contribution of this study lies in the innovative integration of the DE algorithm into teaching evaluation, addressing challenges of objectivity and comprehensiveness that traditional methods often face. The proposed approach enhances evaluation objectivity and provides a systematic tool for educational reform and teaching optimization. The findings of this research are expected to contribute to the ongoing enhancement of the college English teaching quality evaluation system. © 2025 The Author(s).

关键词： optimization algorithms

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Novel objective-based coot puzzle optimisation for overlapping community expansion in complex networks

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International Journal of Networking and Virtual Organisations 2024年第4期31卷 281-305页

作者： Suja, C.K. Harinarayanan, C.V.R. Arivalagan, A. Kalady683574 India Department of Mathematics Government Arts and Science College Thirumangalam Kappalur Tamil Nadu Madurai625008 India PG and Research Department of Mathematics Government Arts College Tamil Nadu Paramakudi623701 India

Overlapping community detection receives more and more interest due to the possibility that certain nodes in real networks belong to numerous communities. However, the majority of the current overlapping community detection approaches significantly minimise the overfitting problems. This work proposes a novel approach for identifying influent nodes and overlapping community expansion in complex networks. Primarily, the identification of influential nodes is identified using the hybrid Renyi entropy and Mutual information (MI). Thereafter, in the overlapping community formation phase, the nodes other than the influential nodes are grouped to form an overlapping community. Finally, overlapping community expansion is accomplished utilising the proposed Coot Puzzle Optimisation Algorithm (CPOA). The proposed CPOA is the incorporation of the Puzzle Optimisation Algorithm (POA) with the Coot algorithm. The proposed CPOA has attained a value of 1.342, 0.549, and 0.634 for minimum Renyi-entropy-based betweenness, fitness of. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： optimization algorithms

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Learning Individualized Hyperparameter Settings

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algorithms 2023年第6期16卷 267-267页

作者： Maniezzo, Vittorio Zhou, Tingting Univ Bologna Dept Comp Sci I-47521 Cesena Italy Univ Sci & Technol Beijing Dept Econ & Management Beijing 100083 Peoples R China

The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific settings. Robust setting optimization is already a mature area of research, while instance-level setting is still in its infancy, with contributions mainly dealing with algorithm selection. The work reported in this paper belongs to the latter category, exploiting the learning and generalization capabilities of artificial neural networks to adapt a general setting generated by state-of-the-art automatic configurators. Our approach differs significantly from analogous ones in the literature, both because we rely on neural systems to suggest the settings, and because we propose a novel learning scheme in which different outputs are proposed for each input, in order to support generalization from examples. The approach was validated on two different algorithms that optimized instances of two different problems. We used an algorithm that is very sensitive to parameter settings, applied to generalized assignment problem instances, and a robust tabu search that is purportedly little sensitive to its settings, applied to quadratic assignment problem instances. The computational results in both cases attest to the effectiveness of the approach, especially when applied to instances that are structurally very different from those previously encountered.

关键词： optimization algorithms inductive learning parameter setting neural network generalization data abstraction combinatorial optimization

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Prediction of lining upward movement during shield tunneling using machine learning algorithms and field monitoring data

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TRANSPORTATION GEOTECHNICS 2023年 41卷

作者： Ye, Xiao-Wei Zhang, Xiao-Long Zhang, Hong-Qian Ding, Yang Chen, Yun-Min Zhejiang Univ MOE Key Lab Soft Soils & Geoenvironm Engn Hangzhou 310058 Peoples R China Zhejiang Univ Dept Civil Engn Hangzhou 310058 Peoples R China

During the construction of shield tunnels, the tunnel lining often has the problem of local or overall upward movement in soft soil areas. Excessive upward movement will lead to lining dislocation, cracks, damage, and even axis deviation. This paper elaborates on how to predict the process of tunnel lining upward movement using machine learning algorithms and field monitoring data systematically. First, fourteen input variables including shield operational parameters, tunnel geometry, geological conditions and anomalous condition are considered to predict the upward displacement of twelve output variables that represent the process of the upward move-ment of the tunnel lining. In addition, 80% field monitoring data (81 datasets) are selected randomly as the training set, and the remaining 20% (20 datasets) are the test set. Then, the average of 5-fold cross validation mean absolute error is regarded as the fitness function of optimization algorithms to find the optimal hyper-parameters. Finally, the prediction performance of four machine learning (ML) algorithms back-propagation neural network (BPNN), general regression neural network (GRNN), extreme learning machine (ELM), and support vector machine (SVM) optimized by particle swarm optimization (PSO) and genetic algorithm (GA) were compared. All ML algorithms except BPNN predicted successfully the trend of upward movement of tunnel lining. In particular, PSO-GRNN accurately captures the evolution of upward displacement in different periods of each ring with the lowest errors and the largest correlation coefficient values.

关键词： Structural health monitoring Shield tunneling construction Upward movement prediction Lining displacement Machine learning (ML) optimization algorithms

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Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

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HELIYON 2023年第11期9卷 e21520页

作者： Faruqui, Nuruzzaman Abu Yousuf, Mohammad Kateb, Faris A. Hamid, Md. Abdul Monowar, Muhammad Mostafa Jahangirnagar Univ Inst Informat Technol IIT Dhaka 1342 Bangladesh Daffodil Int Univ Dept Software Engn Dhaka 1216 Bangladesh King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A -Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.

关键词： Lung cancer classification Convolutional neural network Computer tomography images Internet of medical things Cloud computing optimization algorithms CNN IoMT CT

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