2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第9页

The Non-Walking Triangle Optimization Representation: Enabling Monte Carlo Tree Search-like Methods for Real Parameter Optimization Problems 非行走三角形优化表示法:为实参数优化问题启用蒙特卡罗树搜索方法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660157

Rachel Brown, D. Ashlock

{"title":"The Non-Walking Triangle Optimization Representation: Enabling Monte Carlo Tree Search-like Methods for Real Parameter Optimization Problems","authors":"Rachel Brown, D. Ashlock","doi":"10.1109/SSCI50451.2021.9660157","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660157","url":null,"abstract":"Real parameter estimation is typically performed by an algorithm that operates directly on vectors of real parameters. This study presents an extension of a representation for real parameter optimization that is discrete and based on the iterated partition of simplices, known as the Walking Triangle Representation (WTR), and pairs it with Monte Carlo Tree Search (MCTS)-like algorithms. The number of moves allowed to the WTR is reduced to only its centering move, where a vertex of the simplex is replaced by its center of mass. This representation converts a real parameter optimization to a discrete form, which can then be paired with MCTS-like algorithms. The tree structure of MCTS allows one to keep track of and exploit information from previous attempts (tree extensions) when choosing the next set of moves to try. Six real parameter optimization problems were used to test the algorithm. Four parameters in the algorithm were studied, including: minimum gene length, maximum gene length, number of tree extensions, and probability of exploration (chance). The algorithm regularly performed consistently well, even with a low number of fitness evaluations (typical number of fitness evaluations is up to 3750 per run). This paper focuses on the ability of the Non-Walking Triangle Representation to convert real parameter optimization problems into discrete representations. This concept is demonstrated through the evaluation of the Non-Walking Triangle Monte Carlo Tree Search (MCNon-Walk) algorithm's ability to find optima in a variety of real parameter optimization problems, using differential evolution as a baseline for comparison.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"36 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":"123279277","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

Adaptive Optimal Control of Continuous-Time Linear Systems via Hybrid Iteration 基于混合迭代的连续时间线性系统自适应最优控制

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660016

Omar Qasem, Weinan Gao, T. Bian

引用次数: 5

Improvements to Speed and Efficacy in Non-Stationary Learning in a Flapping-Wing Air Vehicle: Constrained and Unconstrained Flight 扑翼飞行器非平稳学习速度和效率的改进:约束和无约束飞行

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660163

J. Gallagher, Monica Sam

{"title":"Improvements to Speed and Efficacy in Non-Stationary Learning in a Flapping-Wing Air Vehicle: Constrained and Unconstrained Flight","authors":"J. Gallagher, Monica Sam","doi":"10.1109/SSCI50451.2021.9660163","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660163","url":null,"abstract":"Small Flapping-Wing Micro-Air Vehicles (FW-MA Vs) may experience wing damage and wear while in service with even small amounts introducing significant deficits in maintaining path control. Previous work employed a custom Evolutionary Algorithm (EA) that adapted wing motion patterns, while in flight and in normal online service, to compensate for wing damage. Although generally successful in finding solutions to this challenging online non-stationary problem, the previous methods would very often require hours of flight time to reach full success and sometime failed altogether in cases of extreme wing damage. This paper details a new approach that reduces the required learning time by an order of magnitude and extends the range of damage over which one can expect suitable performance. A discussion of what changes were made and why they were made will be provided along with extensive simulation results demonstrating the claims of success. The paper will also provide discussion of what additional work is possible now that both speed and efficacy have been sufficiently improved to support practical in-flight learning in real vehicles.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"27 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":"123650366","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

Initial Population Generation Method and its Effects on MOEA/D 初始种群生成方法及其对MOEA/D的影响

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660097

Cheng Gong, Lie Meng Pang, H. Ishibuchi

引用次数: 1

Decision Support for Infection Outbreak Analysis: the case of the Diamond Princess cruise ship 感染爆发分析的决策支持:以钻石公主号游轮为例

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660140

H. C. R. Oliveira, V. Shmerko, S. Yanushkevich

引用次数: 2

Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms 用传统机器学习算法评估人脸识别中的性别偏见

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660186

Mustafa Atay, Hailey Gipson, Tony Gwyn, K. Roy

引用次数: 3

A Survey of HMM-based Algorithms in Machinery Fault Prediction 基于hmm的机械故障预测算法综述

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659838

Somayeh Bakhtiari Ramezani, Brad Killen, Logan Cummins, S. Rahimi, A. Amirlatifi, Maria Seale

引用次数: 3

A Closed-Loop AR-based BCI for Real-World System Control 一种用于实际系统控制的基于ar的闭环BCI

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659932

Campbell Gorman, Yu-kai Wang

引用次数: 5

Decoding the Confidence Level of Subjects in Answering Multiple Choice Questions Using EEG Induced Capsule Network 利用脑电图诱导胶囊网络解码被试回答多项选择题的信心水平

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659928

Shirsha Bose, Sayantani Ghosh, A. Konar, A. Nagar

{"title":"Decoding the Confidence Level of Subjects in Answering Multiple Choice Questions Using EEG Induced Capsule Network","authors":"Shirsha Bose, Sayantani Ghosh, A. Konar, A. Nagar","doi":"10.1109/SSCI50451.2021.9659928","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659928","url":null,"abstract":"The paper introduces an innovative methodology for the automatic discrimination of multiple choice answers chosen by merit and random guess by analyzing the confidence level of examinees using an Electroencephalographic system. The acquired brain signals of the subjects participating in the experiment are first examined using the eLORETA software which portrays the active participation of the middle frontal gyrus and precuneus when a subject is fully confident regarding the choice of the correct answer. In the next phase, the signals are pre-processed and converted to spectrogram plots using Short Time Fourier Transform (STFT) which reveal the enhanced activation of theta and lower alpha bands when a subject attempts an answer with his/her merit. On the other hand, the afore-said frequency bands portray reduced activation when a subject tries to choose an answer by a mere guess. The acquired spectrogram plots are transferred to a novel Capsule network model that aids in categorizing the two degrees of confidence level: High and Low. The novelty in the design of the Capsule based classifier lies in the introduction of a depthwise separable convolution layer, a squeeze and excitation attention mechanism and a Sigmoid-Weighted Linear Unit (SiLU) based dynamic routing algorithm. The proposed classifier demonstrates promising results in categorizing the two classes of confidence level and also outperforms its conventional counterparts. Thus, the proposed scheme can be utilized to improve the quality of assessment in multiple choice based examinations.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"6 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":"130421846","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

Creating Adjustable Human-like AI Behavior in a 3D Tennis Game with Monte-Carlo Tree Search 用蒙特卡洛树搜索在3D网球游戏中创建可调节的类人AI行为

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659551

Kaito Kimura, Yuan Tu, Riku Tanji, M. Mozgovoy

引用次数: 1