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evolutionary computation for Expensive Optimization:A Survey

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 for solving EOP and related real-world applicatio

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

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evolutionary computation for Large-scale Multi-objective Optimization: A Decade of Progresses

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International Journal of Automation and computing 2021年第2期18卷 155-169页

作者： Wen-Jing Hong Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management Science University of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence Guangzhou 510515China

Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed.

关键词： Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability

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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 discussed.This 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

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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 optimization.However,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformation for optimization of the foot trajectory.Furthermore,the quantitative index of fitness convergence is insufficient.In this paper,we perform gait optimization of a quadruped robot using foot placement perturbation based on EC.The 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 learning.The conventional algorithm and the proposed algorithm are applied to a quadruped robot;walking performances are then compared by gait simulation.Although the two algorithms exhibit similar computation rates,the proposed algorithm shows better fitness and a wider search range.The 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

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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 learning.Generally,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 weights.Several regularization techniques have been proposed so far which have their own advantages and disadvantages.Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques.However,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained solution.In 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 taken.The considered algorithms contain both classical and swarm and evolutionary approaches.For the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are considered.For 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 learning.Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme.To test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are used.Besides,the proposed framework is applied to a real-life identification problem,i.***.identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine,for further assurance on the performance of

关键词： evolutionary computation Function approximation Hybrid systems

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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)

ＥｖｏｌｕｔｉｏｎａｒｙＣｏｍｐｕｔａｔｉｏｎ：ａｎＯｖｅｒｖｉｅｗ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｌｉｍｂｉｎｇＰｒｏｇｒａｍｍｅ┐ＮａｔｉｏｎａｌＫｅｙＰｒｏｊｅｃｔｆｏｒＦｕｎｄａ┐ｍｅ... 详细信息

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

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

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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

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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

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Modeling and Simulation for High Energy Sub-Nuclear Interactions Using evolutionary computation Technique

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Journal of Applied Mathematics and Physics 2016年第1期4卷 53-65页

作者： Mahmoud Y. El-Bakry El-Sayed A. El-Dahshan Amr Radi Mohamed Tantawy Moaaz A. Moussa Department of Physics Faculty of Sciences Ain Shams University Cairo Egypt Egyptian E-Learning University Giza Egypt Department of Physics Faculty of Education Ain Shams University Cairo Egypt The British University in Egypt (BUE) Cairo Egypt Buraydah Colleges East Qassim University Buraydah KSA

High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary computation techniques, the so-called genetic programming (GP), to model the hadron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simulate the rapidity distribution of total charged, positive and negative pions for p--Ar and p--Xe interactions at 200 GeV/c and charged particles for p-pb collision at 5.02 TeV. We have done so many runs to select the best runs of the GP program and finally obtained the rapidity distribution as a function of the lab momentum , mass number (A) and the number of particles per unit solid angle (Y). In all cases studied, we compared our seven discovered functions produced by GP technique with the corresponding experimental data and the excellent matching was so clear.

关键词： Modeling Simulation evolutionary computation Genetic Programming Hadron-Nucleus Interaction Rapidity Distribution

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evolutionary computation for Image Feature Extraction

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Advances in Manufacturing 2000年第4期4卷 295-298页

作者： Radunovic Ljubisa Shuo-zhong Wang School of Communication and Information Engineering Shanghai University Shanghai 200072 China

Modifications to an image feature extraction approach involving evolutionary computation and autonomous agents are proposed. The described algorithm allows extraction of features with certain specified characteristics... 详细信息

关键词： evolutionary computation autonomous agent replacement policy fitness stimulus

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