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A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第5期29卷 2111-2131页

作者： Pourbahman, Zahra Hamzeh, Ali Shiraz Univ Dept Elect & Comp Engn Shiraz Iran

evolutionary algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics-i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.

关键词： Fitness approximation multi-objective evolutionary algorithm fuzzy inference system hypervolume approximation hypervolume contribution

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An evolutionary multitasking Method for High-Dimensional Receiver Operating Characteristic Convex Hull Maximization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1699-1713页

作者： Cheng, Fan Shu, Shengda Zhang, Lei Tan, Ming Qiu, Jianfeng Anhui Univ Inst Informat Mat Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the "curse of dimension". To this end, in this paper, an evolutionary multitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task T-a is constructed to assist the original high-dimensional task T-o. Specifically, in EMT-ROCCH, a low-dimensional assisted task T-a is firstly created. Then, two populations, P-a and P-o, are used to evolve tasks T-a and T-o, respectively. During the evolution, a knowledge transfer from P-a to P-o is designed to transfer the useful knowledge from P-a to accelerate the convergence of P-o. Moreover, a knowledge transfer from P-o to P-a is developed to utilize the useful knowledge in P-o to repair the individuals in P-a, aiming to avoid P-a being trapped into the local optima. Experiment results on 12 high-dimensional datasets have shown that compared with the state-of-the-arts, the proposed EMT-ROCCH could achieve ROCCH with higher quality.

关键词： evolutionary computation evolutionary multitasking ROC convex hull multi-objective evolutionary algorithm knowledge transfer classification

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multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising

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NATURAL COMPUTING 2014年第1期13卷 39-53页

作者： Li, Yangyang Wei, Ying Wang, Yang Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

Synthetic aperture radar (SAR) image segmentation is an important problem of the realm of image segmentation. In this study, a novel SAR image segmentation algorithm using a multi-objective evolutionary algorithm based on decomposition with non-local means denoising (MISD) is proposed. The novelty of MISD lies in the following issues: (1) an effective multi-objective method with decomposition to solve SAR image segmentation;(2) in order to denoise the SAR images and retain the details, we employ non-local means to remove the noise. The multi-objective decomposition method makes MISD have lower computational complexity. In order to evaluate the performance of the new method, we compared the results with three other popular segmentation approaches on four simulated and two real SAR images. In our experiments, the new method can always find better results, which means MISD is a promising SAR image segmentation method.

关键词： multi-objective evolutionary algorithm Clustering SAR image segmentation Non-local means Decomposition approach

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A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems

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INFORMATION SCIENCES 2022年 596卷 236-263页

作者： Xu, Zhiwei Zhang, Kai He, Juanjuan Liu, Xiaoming 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 different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Membrane-inspired evolutionary algorithm Membrane computing evolutionary multitasking multi-objective multi-task optimization multi-objective evolutionary algorithm

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Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第1期58卷 37-49页

作者： Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Carlos Fernandez, Juan Briceno, Javier de la Mata, Manuel Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Univ Hosp Reina Sofia Liver Transplantat Unit Cordoba 14004 Spain

objective: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations. Methods and material: To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs. Results: The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system

关键词： Making decisions rule-based multi-objective evolutionary algorithm Radial basis function neural networks Liver transplantation Organ allocations

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

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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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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

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BMC GENOMICS 2018年第SUPPL 7期19卷 79-92页

作者： Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Ctr Sci Res & Higher Educ Ensenada CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

Background: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. Results: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. Conclusions: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

关键词： Antimicrobial peptides Feature weighting Molecular descriptors Classification multi-objective evolutionary algorithm Peptide representation

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A New Approach Based on a multiobjective evolutionary algorithm for Accurate Control of Flow Rate and Blood Pressure in Cardiac Bioreactors

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CARDIOVASCULAR ENGINEERING AND TECHNOLOGY 2020年第1期11卷 84-95页

作者： Gosselin, Jerome Begin-Drolet, Andre Maciel, Yvan Ruel, Jean Univ Laval Dept Genie Mecan Quebec City PQ G1V 0A6 Canada Pavillon Adrien Pouliot Local 1314-C412245 Quebec City PQ Canada

Purpose-Accurately reproducing physiological and time-varying variables in cardiac bioreactors is a difficult task for conventional control methods. This paper presents a new controller based on a genetic algorithm for the control of a cardiac bioreactor dedicated to the study and conditioning of heart valve substitutes. Methods-A multi-objective genetic algorithm was designed to obtain an accurate simultaneous reproduction of physiological periodic time functions of the three most relevant variables characterizing the blood flow in the aortic valve. These three controlled variables are the flow rate and the pressures upstream and downstream of the aortic valve. Results-Experimental results obtained with this new algorithm showed an accurate dynamic reproduction of these three controlled variables. Moreover, the controller can react and adapt continuously to changes happening over time in the cardiac bioreactor, which is a major advantage when working with living biological valve substitutes. Conclusion-The strong non-linear interaction that exists between the three controlled variables makes it difficult to obtain a precise control of any of these, let alone all three simultaneously. However, the results showed that this new control algorithm can efficiently overcome such difficulties. In the particular field of bioreactors reproducing the cardiovascular environment, such a flexible, versatile and accurate reproduction of these three interdependent controlled variables is unprecedented.

关键词： Genetic algorithm multi-objective evolutionary algorithm Optimization Bioreactor

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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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