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evolutionary computation in Social Propagation over Complex Networks: A Survey

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International Journal of Automation and computing 2021年第4期18卷 503-520页

作者： Tian-Fang Zhao Wei-Neng Chen Xin-Xin Ma Xiao-Kun Wu School of Computer Science&Engineering South China University of TechnologyGuangzhou 510006China School of Journalism and Communication South China University of TechnologyGuangzhou 510006China

Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation(EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are *** paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

关键词： evolutionary computation complex network propagation dynamics social diffusion evolution model optimization algorithm

evolutionary computation for Expensive Optimization:A Survey

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Machine Intelligence Research 2022年第1期19卷 3-23页

作者： Jian-Yu Li Zhi-Hui Zhan Jun Zhang School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Pazhou Laboratory Guangzhou 510330China Victoria University Melbourne 8001Australia

Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas f

关键词： Expensive optimization problem evolutionary computation evolutionary algorithm swarm intelligence particle swarm optimization differential evolution

evolutionary　computation:an　Overview

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通信学报 1997年第3期 71-79页

作者： He Zhenya Wei Chengjian (Department of Radio Engineering,Southeast University,Nanjing 210018)

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ＥｖｏｌｕｔｉｏｎａｒｙＣｏｍｐｕｔａｔｉｏｎ：ａｎＯｖｅｒｖｉｅｗ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｌｉｍｂｉｎｇＰｒｏｇｒａｍｍｅ┐ＮａｔｉｏｎａｌＫｅｙＰｒｏｊｅｃｔｆｏｒＦｕｎｄａ┐ｍｅｎｔａｌＲｅｓｅａｒｃｈｉｎＣｈｉ...

关键词： evolutionary computation evolutionary optimization neural networks fuzzy systems evolable hardware evolutionary robotics

Gait Optimization of a Quadruped Robot Using evolutionary computation

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Journal of Bionic Engineering 2021年第2期18卷 306-318页

作者： Jihoon Kim Dang Xuan Ba Hoyeon Yeom Joonbum Bae Mechanical Engineering Ulsan National Institute of Science and Technology(UNIST)Ulsan 44919South Korea Automatic Control HoChiMinh City University of Technology and Education(HCMUTE)HoChiMinh City 71307Vietnam

evolutionary computation(EC)has strengths in terms of computation for gait ***,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformation for optimization of the foot ***,the quantitative index of fitness convergence is *** this paper,we perform gait optimization of a quadruped robot using foot placement perturbation based on *** proposed algorithm has an atypical solution search range,which is generated by independent manipulation of each placement that forms the foot trajectory.A convergence index is also introduced to prevent premature cessation of *** conventional algorithm and the proposed algorithm are applied to a quadruped robot;walking performances are then compared by gait *** the two algorithms exhibit similar computation rates,the proposed algorithm shows better fitness and a wider search *** evolutionary tendency of the walking trajectory is analyzed using the optimized results,and the findings provide insight into reliable leg trajectory design.

关键词： bionic robot evolutionary computation genetic algorithm gait optimization parameter perturbation convergence index

Recent Advances in evolutionary computation

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Journal of Computer Science & Technology 2006年第1期21卷 1-18页

作者：姚新徐永 Nature Inspired computation and Applications Laboratory The University of Science and Technology of China Hefei 230027 P.R. China The Centre of Excellence for Research in computational Intelligence and Applications (CERCIA)School of Computer Science The University of Birmingham Edgbaston Birmingham B15 2TT UK

evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.

关键词： evolutionary computation neural network ensemble prisoner's dilemma real-world application computational time complexity

Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems

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International Journal of Intelligent Computing and Cybernetics 2014年第4期7卷 346-381页

作者： Ahmad Mozaffari Nasser Lashgarian Azad Alireza Fathi Systems Design Engineering Department University of WaterlooWaterlooCanada Department of Mechanical Engineering University of TechnologyBabolIran

Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine ***,by defining a proper penalty function,regularization laws are embedded into the structure of common least square solutions to increase the numerical stability,sparsity,accuracy and robustness of regression *** regularization techniques have been proposed so far which have their own advantages and *** efforts have been made to find fast and accurate deterministic solvers to handle those regularization ***,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained *** this research,the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine(ELM).Design/methodology/approach–To implement the required tools for comparative numerical study,three steps are *** considered algorithms contain both classical and swarm and evolutionary *** the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are *** swarm and evolutionary-based regularization,an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered,and its algorithmic structure is modified so that it can efficiently perform the regularized *** well-known metaheuristics are considered to check the generalization capability of the proposed *** test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are ***,the proposed framework is applied to a real-life identification problem,*** the dominant factors affecting the hydrocarbon emissions of an automotive eng

关键词： evolutionary computation Function approximation Hybrid systems

A New Algorithm for On-Line Handwriting Signature Verification Based on evolutionary computation

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Wuhan University Journal of Natural Sciences 2006年第3期11卷 596-600页

作者： ZHENG Jianbin ZHU Guangxi Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

The paper proposes an on-line signature verification algorithm, through which test sample and template signatures can be optimizedly matched, based on evolutionary computation （EC）. Firstly, the similarity of signature curve segment is defined, and shift and scale transforms are also introduced due to the randoness of on-line signature. Secondly, this paper puts forward signature verification matching algorithm after establishment of the mathematical model. Thirdly, the concrete realization of the algorithm based on EC is discussed as well. In addition, the influence of shift and scale on the matching result is fully considered in the algorithm. Finally, a computation example is given, and the matching results between the test sample curve and the template signature curve are analyzed in detail, The preliminary experiments reveal that the type of signature verification problem can be solved by EC.

关键词： on-line signature signature verification evolutionary computation

evolutionary computation in China： A literature survey

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CAAI Transactions on Intelligence Technology 2016年第4期1卷 334-354页

作者： Maoguo Gong Shanfeng Wang Wenfeng Liu Jianan Yan Licheng Jiao Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent InJormation Processing Xidian University P.O. Box 224 Xi'an 710071 China

evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese research in theoretical foundations of EC, EC-based optimization, EC-based data mining, and EC-based real-world applications are summarized.

关键词： evolutionary computation evolutionary algorithms Optimization Data mining

Using evolutionary computation to Solve Problems in Nonparametric Regression

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Wuhan University Journal of Natural Sciences 1998年第1期3卷 27-31页

作者： Ding Lixin Kang Lishan Chen Yuping Pan Zhengjun Wuhan University State Key Lab of Software Engineering Wuhan China (GRID:grid.49470.3e) (ISNI:***)

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.

关键词： nonparametric regression Nearest Neighbour Estimate evolutionary computation nonhomogeneous selection adaptive model-building