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Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第3期60卷 663-681页

作者： Deng, Xiongshi Li, Min Deng, Shaobo Wang, Lei Sch Informat Engn Nanchang Inst Technol Nanchang 330099 Jiangxi Peoples R China Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

关键词： XGBoost Feature selection Microarray gene expression multi-objective genetic algorithm Classification

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Optimization and decision-making of novel laser-induced thermal therapy for deep-lying tumor based on multi-objective genetic algorithm and three-way decisions method

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APPLIED MATHEMATICAL MODELLING 2022年 104卷 682-700页

作者： Zhang, Xuelan Che, Yue Zheng, Liancun Shu, Chang Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Energy & Environm Engn Beijing Peoples R China Fuwai Hosp Peking Union Med Coll Chinese Acad Med Sci Natl Ctr Cardiovasc Dis Dept Vascular Surgery 10 0 037 Beijing Peoples R China

In this paper, we develop novel laser-induced thermal therapy (LITT) schemes, multiple and single rotating laser irradiation, for deep-lying breast tumor to maximize tumor killing while minimizing normal tissue damage and preventing the surface overheating without the aid of external media such as nanoparticles and photosensitizers. Combined with numerical simulation and multi-objective genetic algorithm (MOGA), the incident angle, radius, intensity and exposure time of the multiple laser irradiation, as well as the rotational period and exposure time of the single rotating laser irradiation are optimized based on the surface response model, and a series of Pareto-optimal solutions are obtained. The three-way decisions method is utilized to effectively manage the Pareto-optimal solutions and select the ideal solution with a comprehensive consideration of the risk preferences (aversion, proneness and neutralness) of the surgeon. Compared with the traditional LITT, the ideal solution selected by the operator with risk neutralness attitude makes the surface temperature maintain at an acceptable level, and the killing volume of normal tissue is reduced by 80.4% for the multiple laser irradiation and 99.74% for the single rotating laser irradiation in the case of complete tumor killing. Results obtained can be employed as a guideline for the selection of LITT regimens for deep-lying breast tumor under consideration of the surgeon's risk preferences.(c) 2021 Published by Elsevier Inc.

关键词： Deep-lying tumor multiple laser irradiation Single rotating laser irradiation multi-objective genetic algorithm Three-way decisions method

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The Evaluation and Testing System of Automobile Performance Based on multi-objective genetic algorithm

The Evaluation and Testing System of Automobile Performance ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Wen, Yanling Tan, Kecheng Liuzhou Railway Vocational Technical College Guangxi Liuzhou China

ISBN: (纸本)9798350315455

In the face of the rapid growth of China's car ownership, the variety of vehicle types, and the increasingly complex structure and use conditions of modern cars, the majority of car manufacturers urgently need to further control the performance of cars in the process of car design and production, and effectively measure the performance of cars and analyse and process the results so that they have high practicality and economic benefits. The purpose of this paper is to study the vehicle performance evaluation and testing system based on multi-objective genetic algorithm. The indicators for the evaluation of the vehicle performance are determined, including vehicle dynamics, fuel economy, braking, handling stability and environmental friendliness. The fuzzy comprehensive evaluation method is used to establish the subordinate degree function of each factor, so as to obtain the scores of the vehicle performance evaluation, select the appropriate input and output parameters, and establish the evaluation model using the neural network of multi-objective genetic algorithm. As a result, the overall vehicle performance scores after the two sets of real-world tests were 79 and 80 respectively. © 2023 IEEE.

关键词： fuzzy comprehensive evaluation inspection system multi-objective genetic algorithm performance evaluation whole vehicle performance

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Workspace optimization of parallel robot by using multi-objective genetic algorithm

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High Technology Letters 2022年第4期28卷 411-417页

作者： WANG Jinhong LEI Jingtao School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444P.R.China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai 200444P.R.China

For the narrow workspace problem of the universal-prismatic-universal(UPU)parallel robotwith fixed orientation,a kind of multi-objective genetic algorithm is studied to optimize the robot’*** concept of the effective workspace and its solution method are *** effectiveworkspace height(EWH)and global condition number index(GCI)of Jacobi matrix are selected asthe optimized objective *** the robot in two different orientations,the geometric pa-rameters are optimized by the multi-objective genetic algorithm named non-dominated sorting geneticalgorithm II(NSGA-II),and a set of structural parameters is *** optimization results areverified by four indicators with the robot’s moving platform at different *** resultsshow that,after optimization,the fixed-orientation workspace volume,the effective workspace heightand the effective workspace volume increase by 32.4%,17.8%and 72.9%on average,*** decreases by 6.8%on average.

关键词： parallel robot multi-objective genetic algorithm workspace optimization

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MOSAT: Finding Safety Violations of Autonomous Driving Systems using multi-objective genetic algorithm 2022

MOSAT: Finding Safety Violations of Autonomous Driving Syste...

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30th ACM Joint European Software Engineering Conference / Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Tian, Haoxiang Jiang, Yan Wu, Guoquan Yan, Jiren Wei, Jun Chen, Wei Li, Shuo Ye, Dan Univ Chinese Acad Sci State Key Lab Comp Sci ISCAS Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Joint Lab Cyberspace Secur China Southern Power Grid Beijing Peoples R China Univ Chinese Acad Sci Chongqing Sch Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China

ISBN: (纸本)9781450394130

Autonomous Driving Systems (ADSs) are safety-critical systems, and safety violations of Autonomous Vehicles (AVs) in real traffic will cause huge losses. Therefore, ADSs must be fully tested before deployed on real world roads. Simulation testing is essential to find safety violations of ADS. This paper proposes MOSAT, a multi-objective search-based testing framework, which constructs diverse and adversarial driving environment to expose safety violations of ADSs. Specifically, based on atomic driving maneuvers, MOSAT introduces motif pattern, which describes a sequence of maneuvers that can challenge ADS effectively. MOSAT constructs test scenarios by atomic maneuvers and motif patterns, and uses multi-objective genetic algorithm to search for adversarial and diverse test scenarios. Moreover, in order to test the performance of ADS comprehensively during long-mile driving, we design a novel continuous simulation testing technique, which runs the scenarios generated by multiple parallel search processes alternately in the simulator and can continuously create different perturbations to ADS. We demonstrate MOSAT on an industrial-grade platform, Baidu Apollo, and the experimental results show that MOSAT can effectively generate safety-critical scenarios to crash ADSs and it exposes 11 distinct types of safety violations in a short period of time. It also outperforms state-of-the-art techniques by finding more 6 distinct safety violations on the same road.

关键词： Autonomous Driving System Safety Violation multi-objective genetic algorithm

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Using a heuristic multi-objective genetic algorithm to solve the storage assignment problem for CPS-based pick-and-pass system

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ENTERPRISE INFORMATION SYSTEMS 2021年第9期15卷 1238-1259页

作者： Tu, Mengru Yang, Ming-Feng Kao, Sheng-Long Lin, Feng-Cheng Wu, Ming-Hung Lin, Cheng-Kuan Natl Taiwan Ocean Univ Dept Transportat Sci Keelung Taiwan Natl Taiwan Ocean Univ Ctr Excellence Ocean Engn Keelung Taiwan Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan Natl Audit Off Taipei Taiwan

With the advancement of AI and the widespread application of IoT and cloud computing, the pick-and-pass system (PKPS) can potentially be transformed into a cyber-physical system (CPS) based intelligent warehouse picking system. This paper proposes a CPS-based PKPS with a heuristic multi-objective genetic algorithm to solve the NP-hard storage assignment problem (SAP) for order picking operations in an e-commerce-based warehouse. The proposed algorithm considers both the workload balance between picking lines and emergency replenishment during picking operation. Finally, the study shows that the proposed algorithm is effective in improving the efficiency of picking operations based on software simulation.

关键词： Cyber-physical system storage assignment problem pick-and-pass system multi-objective genetic algorithm computer-aided picking systems

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Feature selection with multi-objective genetic algorithm based on a hybrid filter and the symmetrical complementary coefficient

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APPLIED INTELLIGENCE 2021年第6期51卷 3899-3916页

作者： Zhang, Rui Zhang, Zuoquan Wang, Di Du, Marui Beijing Jiaotong Univ Sch Sci Beijing Peoples R China

With the expansion of data size and data dimension, feature selection attracts more and more attention. In this paper, we propose a novel feature selection algorithm, namely, Hybrid filter and Symmetrical Complementary Coefficient based multi-objective genetic algorithm feature selection (HSMOGA). HSMOGA contains a new hybrid filter, Symmetrical Complementary Coefficient which is a well-performed metric of feature interactions proposed recently, and a novel way to limit feature subset's size. A new Pareto-based ranking function is proposed when solving multi-objective problems. Besides, HSMOGA starts with a novel step called knowledge reserve, which precalculate the knowledge required for fitness function calculation and initial population generation. In this way, HSMOGA is classifier-independent in each generation, and its initial population generation makes full use of the knowledge of data set which makes solutions converge faster. Compared with other GA-based feature selection methods, HSMOGA has a much lower time complexity. According to experimental results, HSMOGA outperforms other nine state-of-art feature selection algorithms including five classic and four more recent algorithms in terms of kappa coefficient, accuracy, and G-mean for the data sets tested.

关键词： Feature selection Feature interaction Hybrid filter Symmetrical complementary coefficient multi-objective genetic algorithm

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Integrating the deep learning and multi-objective genetic algorithm to the reloading pattern optimization of HPR1000 reactor core

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NUCLEAR ENGINEERING AND DESIGN 2024年 428卷

作者： Butt, Muhammad Kamran Cao, Liangzhi Wan, Chenghui Lei, Kaihui Khan, Izat Xi An Jiao Tong Univ Sch Nucl Sci & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

The deep learning and multi-objective genetic algorithm were employed to optimize the fuel reloading pattern for HPR1000, a state-of-the-art nuclear power reactor designed and operated in China, also known as Hualong-1. In this study, the deep-learning algorithm was applied to establish the rapid evaluator for fuel-reloading patterns, for which the random samples between fuel-reloading patterns and corresponding key core parameters were generated by our home-developed nuclear-design code, named Bamboo-C. The advanced machine-learning platform TensorFlow was utilized for the deep-learning model. Then, the multi-objective genetic algorithm was applied to search the optimal fuel-reloading patterns, combined with the rapid evaluator to evaluate the key core parameters in a very short time. The DAKOTA toolkit was employed for optimization using a multi-objective genetic algorithm, for which the cycle length and power-peak factors were selected as the target parameters to establish the fitness function. For verification and application, the above method has been applied to the fuel- reloading optimization for Cycle 2 of HPR1000 operated in China. The optimization pattern results in an extension of the cycle length by about 21 EFPD (Effective Full Power Day), keeping all the key safety parameters satisfying corresponding safety criteria.

关键词： Deep-learning algorithm multi-objective genetic algorithm HPR1000 Bamboo-C

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Programming and read performances optimization of phase-change memory via multi-objective genetic algorithm and improved finite element analysis

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MATERIALS SCIENCE IN SEMICONDUCTOR PROCESSING 2024年 169卷

作者： Tang, Pu Tao, Ming Xiao, Jing Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Phase Change Memory (PCM) has attracted widespread attention for its high-speed reading and writing capabilities and high-density non-volatile storage. However, reducing the reset current of PCM can result in an increase in its set resistance, which affects the programming or readout performances. Therefore, further optimization of PCM under this trade-off remains a significant challenge. In this paper, multi-objective genetic algorithm (MOGA) is utilized to address this problem through the optimization of PCM's geometry. An enhanced PCM finite element model, considering the Seebeck, Peltier, and Thomson effects, is employed as the fitness function of MOGA to improve the accuracy of PCM's reset arguments and obtain precise solution. The PCM geometry solution set with optimal performance in different technology nodes are presented in this paper. Specifically, for the 22-nm node, the optimization result shows that the reset current can be reduced by up to 8.7 % when the PCM is employed for low-power applications, such as neuromorphic computing, and the read latency time can be reduced by up to 22.2 % when the PCM is utilized for fast reading purposes, such as in PCM-DRAM main memory. This proposed optimization scheme has great potential in improving the performance of PCM.

关键词： Phase change memory Reset current Set resistance Thermoelectric effect multi-objective genetic algorithm

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Design Optimization of an Iron Core for Cup-Type AMF Contacts Using genetic Aggregation Response Surface and multi-objective genetic algorithm

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Huang, Lei Ma, Hui Wang, Zihan Li, Yuanzhao Geng, Yingsan Wang, Jianhua Liu, Zhiyuan Xian Jiao tong Univ XJTU Dept Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

For cup-type AMF contacts (CACs) in high voltage vacuum interrupters (VIs), the iron core is crucial for enhancing the interrupting capability. The objective of this paper is to employ the genetic Aggregation response surface and multi-objective genetic algorithm for optimizing the AMF characteristics in CACs, which could make the design approach more efficient. Three objectives were chosen for optimization: the highest AMF flux density measured at the central plane within the contact gap (B-AMF), the corresponding phase shift time for B-AMF (t(s)) and the weight of the iron core (m). Finally, an optimal iron core structure was suggested. When comparing the original structure with the optimal one, the AMF in the optimal structure was enhanced across 64.0% of the intermediate plane. Particularly in the central area, the AMF increased by 10.6%. These results demonstrate that significant enhancements were observed in the AMF characteristics of the optimal structure, essential for improving the interrupting capacity of VIs.

关键词： Iron Optimization Response surface methodology Magnetic cores Magnetic fields genetic algorithms Correlation Axial magnetic field cup-type AMF contact design optimization iron core multi-objective genetic algorithm response surface

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