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An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates

evolutionary computation 2023年第1期31卷 53-71页

作者： Hu, Caie Zeng, Sanyou Li, Changhe China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.

关键词： evolutionary computation data-driven evolutionary optimization surrogate model management Non-Gaussian process

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Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3873-3886页

作者： Liang, Jing Qiao, Kangjia Yu, Kunjie Qu, Boyang Yue, Caitong Guo, Weifeng Wang, Ling Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be optimized and various constraints to be satisfied, which challenges the evolutionary algorithms in balancing the objectives and constraints. This article attempts to explore and utilize the relationship between constrained Pareto front (CPF) and unconstrained Pareto front (UPF) to solve CMOPs. Especially, for a given CMOP, the evolutionary process is divided into the learning stage and the evolving stage. The purpose of the learning stage is to measure the relationship between CPF and UPF. To this end, we first create two populations and evolve them by specific learning strategies to approach the CPF and UPF, respectively. Then, the feasibility information and dominance relationship of the two populations are used to determine the relationship. Based on the learned relationship, specific evolving strategies are designed in the evolving stage to improve the utilization efficiency of objective information, so as to better solve this CMOP. By the above process, a new constrained multiobjective evolutionary algorithm (CMOEA) is presented. Comprehensive experimental results on 65 benchmark functions and ten real-world CMOPs show that the proposed method has a better or very competitive performance in comparison with several state-of-the-art CMOEAs. Moreover, this article demonstrates that using the relationship between CPF and UPF to guide the utilization of objective information is promising in solving CMOPs.

关键词： Statistics Sociology Optimization Linear programming Search problems evolutionary computation Shape Constrained multiobjective optimization evolutionary algorithm objective function relationship

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Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts

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IEEE TRANSACTIONS ON evolutionary computation 2023年第1期27卷 98-110页

作者： Li, Wenhua Yao, Xingyi Zhang, Tao Wang, Rui Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multi Energy Syst Intelligent Interc Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value. Traditional multimodal multiobjective evolutionary algorithms (MMEAs) prefer to pursue multiple Pareto solutions that have the same objective values. However, a more practical situation in engineering problems is that one solution is slightly worse than another in terms of objective values, while the solutions are far away in the decision space. In other words, such problems have global and local Pareto fronts (PFs). In this study, we proposed several benchmark problems with several local PFs. Then, we proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision maker's preference. Regarding HREA, we proposed a local convergence quality evaluation method to better maintain diversity in the decision space. Moreover, a hierarchy ranking method was introduced to update the convergence archive. The experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.

关键词： Convergence Optimization Benchmark testing Clustering algorithms Systems engineering and theory Statistics Space missions Diversity-preserving mechanisms evolutionary computation local Pareto front (PF) multimodal multiobjective optimization

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Graph-Based Feature Selection in Classification: Structure and Node Dynamic Mechanisms

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2023年第4期7卷 1314-1328页

作者： Cheng, Fan Zhou, Changjun Liu, Xudong Wang, Qijun Qiu, Jianfeng Zhang, Lei Anhui Univ Sch artificial intelligence Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China

Recently, researchers pay more attention to designing graph-based methods to address the feature selection problem, since these methods can effectively utilize the underlying topology structure and complex relationships between nodes in the constructed feature graph. Therefore, they can obtain the feature subset with high quality. The existing graph-based methods mainly focus on using different graph-theoretical techniques to select features from the constructed feature graphs. However, little attention is focused on constructing a suitable feature graph for feature selection, which is also an important component for achieving a good feature subset. To fill the gap, in this paper, a novel graph-based algorithm named GBFS-SND is proposed for feature selection, where the structure and node dynamic mechanisms are designed to directly optimize the performance of feature selection. To be specific, in GBFS-SND, a candidate feature graph is firstly created by considering both the importance of feature and the relations between features. Then, on the created candidate graph, an MOEA-based structure dynamic mechanism is suggested to acquire a feature subgraph with better structure, from which we can obtain a promising feature subset. Finally, a node dynamic mechanism is also suggested, with which the weights of the nodes are dynamically adjusted as the structure of feature graph changes. Thus, the performance of GBFS-SND can be further enhanced. Empirical studies are conducted by comparing the proposed algorithm with several state-of-the-art feature selection methods on different data sets. The experimental results demonstrate the superiority of GBFS-SND over the comparison methods in terms of both the accuracy and the number of selected features.

关键词： Feature selection graph theory multi-objective optimization evolutionary computation classification

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evolutionary Optimization of COVID-19 Vaccine Distribution With evolutionary Demands

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IEEE TRANSACTIONS ON evolutionary computation 2023年第1期27卷 141-154页

作者： Zheng, Yu-Jun Chen, Xin Song, Qin Yang, Jun Wang, Ling Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Zhejiang Univ Coll Publ Hlth Hangzhou 310058 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.

关键词： Vaccines COVID-19 Satellites evolutionary computation Urban areas Metaheuristics Costs evolutionary optimization hybrid machine learning and evolutionary computation novel coronavirus pneumonia (COVID-19) vaccine distribution vehicle routing

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evolutionary Multi-Objective Optimization in Searching for Various Antimicrobial Peptides [Feature]

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IEEE computationAL INTELLIGENCE MAGAZINE 2023年第2期18卷 31-45页

作者： Liu, Yiping Zhang, Xinyi Liu, Yuansheng Su, Yansen Zeng, Xiangxiang Yen, Gary G. Hunan Univ Changsha Peoples R China Anhui Univ Hefei Peoples R China Oklahoma State Univ Stillwater OK USA

Antimicrobial peptides (AMPs), which are parts of the innate immune response found among all classes of life, are promising in broad-spectrum antibiotics and drug-resistant infection treatments. Although AMPs effectively kill bacteria, numerous AMPs widely distributed in the sequence space remain unknown to humans. Therefore, the de novo design of AMPs involves the exploration of vast sequence space to identify peptides with high antimicrobial activity and good diversity among the known AMPs. computational intelligence approaches have successfully identified some AMPs;however, most of them fail to address the diversity of the obtained AMPs. This paper reports an evolutionary multi-objective approach for AMP design to optimize both the antimicrobial activity and diversity among identified AMPs. Our approach employs a deep learning model to predict a peptide's antimicrobial activity and a niche sharing method to estimate a peptide's density. Then, an evolutionary multi-objective algorithm is presented to simultaneously optimize the objectives of antimicrobial activity and diversity. The algorithm takes the advantage of a decomposition-based framework to search for AMPs with good diversity. These AMPs are collected by an elite archive during the evolution process. Moreover, a local search strategy is applied to enhance the quality of the identified AMPs. The experimental results show that the proposed approach outperforms the state-of-the-art designs in searching for various AMPs. The AMPs generated by the proposed approach have high antimicrobial activities and are distinct from each other and among the AMPs in the datasets.

关键词： Deep learning Toxicology Peptides evolutionary computation Predictive models Prediction algorithms Search problems Immune system

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A deep hybrid transfer learning-based evolutionary algorithm and its application in the optimization of high-order problems

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SOFT COMPUTING 2023年第14期27卷 9661-9672页

作者： Zhang, Ting-Ting Hao, Guo-Sheng Lim, Meng-Hiot Gu, Feng Wang, Xia Jiangsu Normal Univ Sch Comp Sci & Technol 101 Shanghai Rd Xuzhou 221116 Jiangsu Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Jurong West Singapore 639798 Singapore CUNY Coll Staten Isl Dept Comp Sci 2800 Victory Blvd New York NY 10314 USA

High-order problems pose significant challenges for evolutionary algorithms (EAs) to optimize. To mitigate this, a deep hybrid transfer learning EA (DHTL-EA) is proposed. DHTL-EA works by transferring both the model and the optima from a corresponding low-order problem. Here, a deep neural network is adopted to model both the low-order and high-order problems, and the training data are derived from historical evolutionary data. The theoretical basis of DHTL-EA is well-supported by the transferability of solutions among domination-landscape-equivalent problems. The transfer to high-order problem is achieved by retraining the last fully connected layer of the deep neural network model for the target problem. Experiments on two groups of problems validated that DHTL-EA is effective on high-order problems. Further testing on other benchmark functions demonstrated its competitive performance.

关键词： evolutionary computation Deep learning Transfer learning High-order function Optimization

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A Comprehensive Survey on Program Synthesis With evolutionary Algorithms

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IEEE TRANSACTIONS ON evolutionary computation 2023年第1期27卷 82-97页

作者： Sobania, Dominik Schweim, Dirk Rothlauf, Franz Johannes Gutenberg Univ Mainz Informat Syst Dept D-55099 Mainz Germany Baden Wuerttemberg Cooperat State Univ Informat Syst Dept D-89518 Heidenheim Germany

The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques not only software developers could be supported in their everyday work but even users without any programming knowledge could be empowered to automate repetitive tasks and implement their own new functionality. In recent years, many novel program synthesis approaches based on evolutionary algorithms have been proposed and evaluated on common benchmark problems. Therefore, we identify and discuss in this survey the relevant evolutionary program synthesis approaches in the literature and provide an in-depth analysis of their performance. The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming (GP). For the stack-based approaches, we identify 37 in-scope papers, and for the grammar-guided and linear GP approaches, we identify 12 and 5 papers, respectively. Furthermore, we find that these approaches perform well on benchmark problems if there is a simple mapping from the given input to the correct output. On problems where this mapping is complex, e.g., if the problem consists of several subproblems or requires iteration/recursion for a correct solution, results tend to be worse. Consequently, for future work, we encourage researchers not only to use a program's output for assessing the quality of a solution but also the way toward a solution (e.g., correctly solved subproblems).

关键词： Benchmark testing evolutionary computation Programming Natural languages Codes Python Task analysis Benchmarks evolutionary algorithms genetic programming (GP) program synthesis

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Automated Feature Selection: A Reinforcement Learning Perspective

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 2272-2284页

作者： Liu, Kunpeng Fu, Yanjie Wu, Le Li, Xiaolin Aggarwal, Charu Xiong, Hui Univ Cent Florida Orlando FL 32816 USA Hefei Univ Technol Hefei 230002 Peoples R China Nanjing Univ Nanjing 210023 Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Rutgers State Univ New Brunswick NJ 08901 USA

Feature selection is a critical step in machine learning that selects the most important features for a subsequent prediction task. Effective feature selection can help to reduce dimensionality, improve prediction accuracy, and increase result comprehensibility. It is traditionally challenging to find the optimal feature subset from the feature subset space as the space could be very large. While much effort has been made on feature selection, reinforcement learning can provide a new perspective towards a more globally-optimal searching strategy. In the preliminary work, we propose a multi-agent reinforcement learning framework for the feature selection problem. Specifically, we first reformulate feature selection with a reinforcement learning framework by regarding each feature as an agent. Besides, we obtain the state of the environment in three ways, i.e., statistic description, autoencoder, and graph convolutional network (GCN), in order to derive a fixed-length state representation as the input of reinforcement learning. In addition, we study how the coordination among feature agents can be improved by a more effective reward scheme. Also, we provide a GMM-based generative rectified sampling strategy to accelerate the convergence of multi-agent reinforcement learning. Our method searches the feature subset space more globally and can be easily adapted to real-time scenarios due to the nature of reinforcement learning. In the extended version, we further accelerate the framework from two aspects. From the sampling aspect, we show the indirect acceleration by proposing a rank-based softmax sampling strategy. From the exploration aspect, we show the direct acceleration by proposing an interactive reinforcement learning (IRL)-based exploration strategy. Extensive experimental results show the significant improvement of the proposed method over conventional approaches.

关键词： Reinforcement learning Feature extraction Task analysis Training Prediction algorithms evolutionary computation Predictive models Feature selection multi-agent reinforcement learning interactive reinforcement learning

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An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles

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ENGINEERING WITH COMPUTERS 2023年第6期39卷 4093-4101页

作者： Asr, A. Ahangar Johari, A. Javadi, A. A. Univ Salford Sch Sci Engn & Environm SEE Manchester M5 4WT England Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 Devon England

The discharge flow rate beneath sheet plies is an essential parameter in designing these water retaining structures. This paper presents a unified framework for modeling and predicting discharge flow rate using an evolutionary-based polynomial regression technique. EPR (evolutionary Polynomial Regression) is a data-driven method based on evolutionary computing to search for polynomial structures representing a system. The input parameters in the modeling procedure included the sheet pile height, upstream water head, and the hydraulic conductivity anisotropy ratio. Due to ever-increasing demand for water, a widely held view on predicting and controlling the available water behind reservoirs, dams, barrages, and weirs is of vital importance. To this end, the sheer novelty of the current study has been worn off through the development of a comprehensive model to predict the flow rate considering the most effective variables in the seepage issue. To the best of our knowledge, the research conducted in the literature has yet to cover the whole seepage problem using a comprehensive database extracted by numerical methods;thus, a comprehensive finite-element-based artificial database including 1000 data lines was created using the Scaled Boundary Finite Element Method (SBFEM) by simulating seepage beneath sheet plies covering a considerably wide range of seepage-related real-world values. The database was then employed to develop and validate the EPR flow rate prediction model. Data were divided into training (used for creating the models) and testing (for validating the developed models) data based on a statistical process. The procedure for preparing the data and developing and validating the models is presented in detail in this paper. The main advantage of the proposed models over a conventional and neural network and most GP (Genetic Programming)-based constitutive models is that they provide the optimum structure for the material constitutive model representation as

关键词： Sheet piles cut-off walls Seepage flow rate evolutionary computation Data mining

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