2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第5页

On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control 基于聚类辅助迁移和自适应摄动矢量控制改进SOMA算法开发阶段的改进

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659916

T. Kadavy, Adam Viktorin, Michal Pluhacek, S. Kovár

引用次数: 2

A Comparative Study on Population-Based Evolutionary Algorithms for Multiple Traveling Salesmen Problem with Visiting Constraints 具有访问约束的多旅行推销员问题的种群进化算法比较研究

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660021

Cong Bao, Qiang Yang, Xudong Gao, Jun Zhang

{"title":"A Comparative Study on Population-Based Evolutionary Algorithms for Multiple Traveling Salesmen Problem with Visiting Constraints","authors":"Cong Bao, Qiang Yang, Xudong Gao, Jun Zhang","doi":"10.1109/SSCI50451.2021.9660021","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660021","url":null,"abstract":"The multiple traveling salesmen problem with visiting constraints (VCMTSP) is an extension of the multiple traveling salesmen problem (MTSP). In this problem, some cities are restricted to be only accessed by certain salesmen, which is very common in real-world applications. In the literature, evolutionary algorithms (EAs) have been demonstrated to effectively solve MTSP. In this paper, we aim to adapt three widely used EAs in solving MTSP, namely the genetic algorithm (GA), the ant colony optimization algorithm (ACO), and the artificial bee colony algorithm (ABC), to solve VCMTSP. Then, we conduct extensive experiments to investigate the optimization performance of the three EAs in solving VCMTSP. Experimental results on various VCMTSP instances demonstrate that by means of its strong local exploitation ability, ABC shows much better performance than the other two algorithms, especially on large-scale VCMTSP. Though GA and ACO are effective to solve small-scale VCMTSP, their effectiveness degrades drastically on large-scale instances. Particularly, it is found that local exploitation is very vital for EAs to effectively solve VCMTSP. With the above observations, it is expected that this paper could afford a basic guideline for new researchers who want to take attempts in this area.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116248623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Feature Selection for Fuzzy Neural Networks using Group Lasso Regularization 基于群Lasso正则化的模糊神经网络特征选择

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659548

Tao Gao, Xiao Bai, Liang Zhang, Jian Wang

引用次数: 1

Monte Carlo Skill Estimation for Darts 蒙特卡罗技能估计的飞镖

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659951

Thomas Miller, Christopher Archibald

{"title":"Monte Carlo Skill Estimation for Darts","authors":"Thomas Miller, Christopher Archibald","doi":"10.1109/SSCI50451.2021.9659951","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659951","url":null,"abstract":"In physical games, like darts, the ability of a player to accurately execute an intended action has a significant impact on their success. Determining this execution precision, or skill, for players is thus an important task. Knowledge of skill can be used for player feedback, computer-aided strategy decisions, game handicapping, and opponent modeling. Challenges to estimating player ability include getting precise feedback on executed actions as well as performing the estimation in a natural and user-friendly way. A previous method for estimating skill in darts overcomes the first challenge, but falls short on the second, requiring players to throw 50 darts at the center of the dartboard, which is not a common target in most darts games. In this paper we present an extension of this previous method that enables skill to be estimated when darts are aimed anywhere, not just the center of the dartboard. This method is then utilized to develop a much more efficient and adaptive skill estimation method which requires far fewer darts than the previous method. Experimental results demonstrate the advantages of the proposed approach and additional possible applications are discussed.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"42 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization 基于并行种群的高维黑盒优化模拟退火

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659957

Youkui Zhang, Qiqi Duan, Chang Shao, Yuhui Shi

{"title":"Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization","authors":"Youkui Zhang, Qiqi Duan, Chang Shao, Yuhui Shi","doi":"10.1109/SSCI50451.2021.9659957","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659957","url":null,"abstract":"In this paper, we present a simple yet efficient parallel version of simulated annealing (SA) for large-scale black-box optimization within the popular population-based framework. To achieve scalability, we adopt the island model, commonly used in parallel evolutionary algorithms, to update and communicate multiple independent SA instances. For maximizing efficiency, the copy-on-write operator is used to avoid performance-expensive lock when different instances exchange solutions. For better local search ability, individual step sizes are dynamically adjusted and learned during decomposition. Furthermore, we utilize the shared memory to reduce data redundancy and support concurrent fitness evaluations for challenging problems with costly memory consumption. Experiments based on the powerful Ray distributed computing library empirically demonstrate the effectiveness and efficiency of our parallel version on a set of 2000-dimensional benchmark functions (especially each is rotated with a 2000*2000 orthogonal matrix). To the best of our knowledge, these rotated functions with a memory-expensive data matrix were not tested in all previous works which considered only much lower dimensions. For reproducibility and benchmarking, the source code is made available at https://github.com/Evolutionary-Intelligence/PPSA.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129398521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy Preserving Modified Projection Subgradient Algorithm for Multi-Agent Online Optimization 保护隐私的改进投影子梯度多智能体在线优化算法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659902

Jiaojiao Yan, Jinde Cao

引用次数: 1

Predicting Patient Discharge Disposition in Acute Neurological Care 预测病人出院处置在急性神经护理

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659904

Charles F. Mickle, D. Deb

{"title":"Predicting Patient Discharge Disposition in Acute Neurological Care","authors":"Charles F. Mickle, D. Deb","doi":"10.1109/SSCI50451.2021.9659904","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659904","url":null,"abstract":"Acute neurological complications are some of the leading causes of death and disability in the U.S. and the medical professionals that treat patients in this setting are tasked with deciding where (e.g., home or facility), how, and when to discharge these patients. It is important to be able to predict ahead of time these potential patient discharge outcomes and to know what factors influence the development of discharge planning for such adults receiving care for neurological conditions in an acute setting. The goal of this study is to develop predictive models exploring which patient characteristics and clinical variables significantly influence discharge planning with the hope that the models can be used in a suggestive context to help guide healthcare providers in efforts of planning effective, equitable discharge recommendations. Our methodology centers around building and training five different machine learning models followed by testing and tuning those models to find the best-suited predictor with a dataset of 5,245 adult patients with neurological conditions taken from the eICU-CRD database. The results of this study show XGBoost to be the most effective model for predicting between four common discharge outcomes of ‘home’, ‘nursing facility’, ‘rehab’, and ‘death’, with 71% average c-statistic. This research also explores the accuracy, reliability, and interpretability of the best performing model by identifying and analyzing the features that are most impactful to the predictions.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129338612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Tuning Free Approach to Multi-guide Particle Swarm Optimization 多导粒子群优化的无调优方法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660050

Kyle Erwin, A. Engelbrecht

引用次数: 0

Set-based Particle Swarm Optimization for Portfolio Optimization with Adaptive Coordinate Descent Weight Optimization 基于集合粒子群的自适应坐标下降权优化组合优化

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659541

Kyle Erwin, A. Engelbrecht

{"title":"Set-based Particle Swarm Optimization for Portfolio Optimization with Adaptive Coordinate Descent Weight Optimization","authors":"Kyle Erwin, A. Engelbrecht","doi":"10.1109/SSCI50451.2021.9659541","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659541","url":null,"abstract":"Set-based algorithms have been shown to successfully find optimal solutions to the portfolio optimization problem and to scale well to larger portfolio optimization problems. Set-based algorithms work by selecting a sub-set of assets from the set universe. These assets then form a new search space where the asset weights are optimized. Erwin and Engelbrecht purposed such an algorithm that was shown to perform similarly to a well known genetic algorithm for portfolio optimization. The proposed algorithm, set-based particle swarm optimization (SBPSO), used a meta-huerstic for the weight optimization process - unlike previous set-based approaches to portfolio optimization. Erwin and Engelbrecht also developed several modifications to SBPSO that improved its performance for portfolio optimization. This paper investgates an alternative weight optimizer for SBPSO for portfolio optimization, namely adaptive coordinate descent (ACD). ACD is a completely deterministic approach and thus ensures that, after a finite time, an approximation of a global optimum will be found. It is shown that SBPSO for portfolio optimization using ACD for weight optimization found higher quality solutions than the current SBPSO algorithm, albeit slightly slower.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Supervised Noise Reduction for Clustering on Automotive 4D Radar 汽车四维雷达聚类的监督降噪方法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659953

Michael Lutz, Monsij Biswal

{"title":"Supervised Noise Reduction for Clustering on Automotive 4D Radar","authors":"Michael Lutz, Monsij Biswal","doi":"10.1109/SSCI50451.2021.9659953","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659953","url":null,"abstract":"In the automotive industry, radar technology is an essential component for object identification due to its low cost and robust accuracy in harsh weather conditions. Clustering, an unsupervised machine learning technique, groups together individual radar responses to detect objects. Because clustering is a significant step in the automotive object identification pipeline, cluster quality and speed are especially critical. To that extent, density-based clustering algorithms have made significant progress due to their ability to operate on data sets with an unknown quantity of clusters. However, many density-based clustering algorithms such as DBSCAN remain unable to deal with inherently noisy radar data. Furthermore, many existing algorithms are not adapted to operate on state-of-the-art 4D radar systems. Thus, we propose a novel pipeline that utilizes supervised machine learning to predict noisy points on 4D radar point clouds by leveraging historical data. We then input noise predictions into two proposed cluster formation approaches, respectively involving dynamic and fixed search radii. Our best performing model performs roughly 153 percent better than the baseline DBSCAN in terms of V-Measure, and our quickest model finishes in 75 percent less time than DBSCAN while performing 130 percent better in terms of V-Measure.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129063020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0