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E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments

NEUROCOMPUTING 2020年 415卷 60-73页

作者： Garcia-Vico, Angel Miguel Charte, Francisco Gonzalez, Pedro Elizondo, David Carmona, Cristobal Jose Univ Jaen Interuniv Andalusian Inst Data Sci & Computat Int Jaen 23071 Spain De Monfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England De Monfort Univ Leicester Sch Pharm Leicester LE1 9BH Leics England

In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process. The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Emerging pattern mining evolutionary fuzzy systems multi-objective evolutionary algorithm Big data

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AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction

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KNOWLEDGE-BASED SYSTEMS 2018年 146卷 58-72页

作者： Song, Shuangbao Gao, Shangce Chen, Xingqian Jia, Dongbao Qian, Xiaoxiao Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

Despite half-century's unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to solving the protein structure prediction (PSP) problem: an effective energy function and an efficient conformation search strategy. In this study, we model the PSP as a multi-objective optimization problem. A three-objective evolution algorithm called AIMOES is proposed. AIMOES adopts three physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. In AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search. A decision maker based on the hierarchical clustering is carried out to select representative solutions. A set of benchmark proteins with 30-91 residues is tested to verify the performance of the proposed method. Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mutation operators. The superiority of AIMOES is demonstrated by the performance comparison with other five state-of-the-art PSP methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Reuse search experience Hierarchical clustering Protein structure prediction

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An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks

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NEUROCOMPUTING 2018年 273卷 346-356页

作者： Lin, Xianghong Li, Zhiqiang Ma, Huifang Wang, Xiangwen Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Gansu Peoples R China Wuhan Univ Coll Comp State Key Lab Software Engn Wuhan 430072 Hubei Peoples R China

Computational modeling of neuronal morphologies is significant for understanding structure-function relationships and brain information processing in computational neuroscience. Using a gene regulatory network model, an evolutionary developmental approach is presented for efficient generation of 3D virtual neurons. This approach describes the developmental process of dendritic morphologies by locally inter-correlating morphological variables which can be represented by the dynamics of gene expression. Then, the multi-objective evolutionary algorithm with gene segmental duplication and divergence operators is applied to evolve the virtual neurons, which aims at generating virtual neurons that are as good as the experimentally traced real neurons in terms of statistical morphological measurements. We experimentally generated motoneurons and statistically compared between the real neurons and the generated virtual neurons by measuring a series of emergent morphological features. The results show that the generated virtual neurons are seemingly realistic, accurate, and further suggest that this approach is an efficient tool for understanding neural development and investigating the relation of neuronal structure to function in particular. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Virtual neuron Neuronal morphology Gene regulatory network Artificial genome multi-objective evolutionary algorithm

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An adaptive multiobjective evolutionary algorithm based on grid subspaces

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MEMETIC COMPUTING 2021年第2期13卷 249-269页

作者： Li, Linlin Wang, Xianpeng Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China Liaoning Key Lab Mfg Syst & Logist Liaoning Engn Lab Operat Analyt & Optimizat Smart Shenyang 110819 Peoples R China

The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.

关键词： Grid External archive multi-objective evolutionary algorithm Dominance relationship

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Using evolutionary algorithms to determine the residual stress profile across welds of age-hardenable aluminum alloys

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APPLIED SOFT COMPUTING 2016年 40卷 429-438页

作者： Ignacio Hidalgo, J. Fernandez, Ricardo Manuel Colmenar, J. Cioffi, Florencia Risco-Martin, Jose L. Gonzalez-Doncel, Gaspar Univ Complutense Madrid Adapt & Bioinspired Syst Grp Calle Prof Jose Garcia Santesmases 9 E-28040 Madrid Spain CSIC CENIM Ctr Nacl Invest Met Dept Met Phys Av Gregorio del Amo 8 Madrid 28040 Spain Univ Rey Juan Carlos Calle Tulipan Mostoles 28933 Spain

This paper presents an evolutionary based method to obtain the un-stressed lattice spacing, do, required to calculate the residual stress profile across a weld of an age-hardenable aluminum alloy, AA2024. Due to the age-hardening nature of this alloy, the do value depends on the heat treatment. In the case of welds, the heat treatment imposed by the welding operation differs significantly depending on the distance to the center of the joint. This implies that a variation of do across the weld is expected, a circumstances which limits the possibilities of conventional analytical methods to determine the required do profile. The interest of the paper is, therefore, two-fold: First, to demonstrate that the application of an evolutionary algorithm solves a problem not addressed in the literature such as the determination of the required data to calculate the residual stress state across a weld. Second, to show the robustness of the approximation used, which allows obtaining solutions for different constraints of the problem. Our results confirm the capacity of evolutionary computation to reach realistic solutions under three different scenarios of the initial conditions and the available experimental data. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Real world application Residual stress determination

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A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

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ADVANCES IN WATER RESOURCES 2016年 92卷 172-185页

作者： Salazar, Jazmin Zatarain Reed, Patrick M. Herman, Jonathan D. Giuliani, Matteo Castelletti, Andrea Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use evolutionary multi-objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e. g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The epsilon-MOEA, the auto-adaptive Borg MOEA, and epsilon-NSGAII all yielded superior results for the six-objective Lower Susquehanna bench-marking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs;however algorithmic choice remains a key concern for problems of increasing complexity. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-purpose reservoir control Direct policy search multi-objective evolutionary algorithm Benchmark

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Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition

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SWARM AND evolutionary COMPUTATION 2020年 54卷 100668-000页

作者： Wan, Xing Zuo, Xingquan Song, Feng Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

Dynamic and overlapping are two common features of community structures for many real world complex networks. Although there are few studies on detecting dynamic overlapping communities, all those studies only consider a single optimization objective. In practice, it is necessary to evaluate the community detection by multiple metrics to reflect different aspects of a community structure and those metrics may conflict with each other. In this paper, we propose a multi-objective approach based on decomposition for the problem of dynamic overlapping community detection, with consideration of three optimization objectives: partition density (D), extended modularity (EQ), and improved mutual information (NMILFK). The dynamic overlapping network can be regarded as a set of network snapshots. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to detect the communities for each snapshot. To improve the search efficiency, the dynamic optimization technique and a dynamic resource allocation strategy are introduced into the approach. Experiments show that our approach can find uniformly distributed Pareto solutions for the problem and outperforms those comparative approaches.

关键词： multi-objective evolutionary algorithm Overlapping community detection Dynamic community detection Dynamic optimization

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A New evolutionary multitasking algorithm for High-Dimensional Feature Selection

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IEEE ACCESS 2024年 12卷 89856-89872页

作者： Liu, Ping Xu, Bangxin Xu, Wenwen Anhui Elect Power Design Inst Co Ltd China Energy Engn Grp Hefei 230601 Peoples R China

Feature selection (FS) is an important dimension reduction technique in practical applications, which has been widely studied during the past decades. Although a large number of FS algorithms have been proposed and shown the promising performance, most of them face with the challenge of "curse of dimensionality". To this end, inspired by evolutionary multitasking (EMT), in this paper, a VariAble multiTasking-based multi-objective evolutionary algorithm, named VAMT-MOEA, is proposed for high-dimensional FS. For the existing EMT-based FS algorithms, they adopt the single or fixed assisted tasks to solve the high-dimensional FS problem (namely the original task). Once they trap into the local optima, it is difficult for them to provide valuable knowledge. Different from them, the proposed algorithm employs the variable multitasking scheme to achieve the feature subsets with high quality. The assisted task is adjusted with the changes of the weights in the evolution, where the weight measures the importance of each feature. Specifically, a variable-weight adjustment strategy is proposed to adjust the assisted task, aiming to overcome the loss of diversity during the evolution. Additionally, a novel knowledge transfer strategy is suggested, where the best and the worst solutions are used to implement the positive knowledge transfer between the assisted task and the original task. Finally, an initialization strategy is designed to generate an initial assisted task with competitiveness. The experimental results on 10 high-dimensional datasets, whose dimension ranges from 2,000 to 19,993, demonstrate the superiority of the proposed VAMT-MOEA in terms of the classification error, the number of selected features and the running time. To be specific, compared with five EA-based FS algorithms, the proposed VAMT-MOEA can achieve the feature subsets with lower classification error and smaller number of features. Moreover, the running time of different algorithms also reveal that

关键词： Task analysis multitasking Feature extraction Statistics Social factors Optimization Knowledge transfer evolutionary computation evolutionary multitasking feature selection high-dimensional data multi-objective evolutionary algorithm knowledge transfer

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Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

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INFORMATION SCIENCES 2022年 582卷 215-242页

作者： Xu, Zhiwei Liu, Xiaoming Zhang, Kai He, Juanjuan Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been pro-posed to solve multi-objective multi-task optimization problems. However, EMT algo-rithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission the-ory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller popula-tion size and fewer computing resources. As a result, the proposed algorithm can effec-tively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative trans-fer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithm evolutionary multitasking multi-task optimization Cultural transmission Evolution strategy

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Enhanced θ dominance and density selection based evolutionary algorithm for many-objective optimization problems

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APPLIED INTELLIGENCE 2018年第4期48卷 992-1012页

作者： Zhou, Chong Dai, Guangming Wang, Maocai China Univ Geosci Sch Comp Wuhan 430074 Hubei Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) have been developed for many-objective optimization. This paper proposes a new enhanced oee integral dominance and density selection based evolutionary algorithm (called oee integral-EDEA) for many-objective optimization problems. We firstly construct an m-dimension hyper-plane using the extreme point on the each dimension. Then we replace the distance between the origin point and projection of solution on the reference line of oee integral dominance which recently is proposed in oee integral dominance based evolutionary algorithm (oee integral-DEA), with the perpendicular distance between each solution and the hyper-plane to develop an enhanced oee integral dominance. Finally, in order to maintain better diversity, oee integral-EDEA employs density based selection mechanism to select the solution for the next population in the environment selection phase. oee integral-EDEA still inherits clustering operator and ranking operator of oee integral-DEA to balance diversity and convergence. The performance of oee integral-EDEA is validated and compared with five state-of-the-art algorithms on two well-known many-objective benchmark problems with three to fifteen objectives. The results show that oee integral-EDEA is capable of obtaining a solution set with better convergence and diversity.

关键词： Many-objective Optimization multi-objective evolutionary algorithm theta dominance m-dimension hyper-plane Density

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