2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第2页

A Topologically Consistent Visualization of High Dimensional Pareto-front for Multi-Criteria Decision Making 面向多准则决策的高维Pareto-front拓扑一致可视化

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628892

A. K. A. Talukder, K. Deb

{"title":"A Topologically Consistent Visualization of High Dimensional Pareto-front for Multi-Criteria Decision Making","authors":"A. K. A. Talukder, K. Deb","doi":"10.1109/SSCI.2018.8628892","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628892","url":null,"abstract":"There are a good number of different algorithms to solve multi- and many-objective optimization problems and the final outcome of these algorithms is a set of trade-off solutions that are expected to span the entire Pareto-front. Visualization of a Pareto-front is vital for an initial decision-making task, as it provides a number of useful information, such as closeness of one solution to another, trade-off among conflicting objectives, localized shape of the Pareto-front vis-a-vis the entire front, and others. Two and three-dimensional Pareto-fronts are trivial to visualize and allow all the above analysis to be done comprehensively. However, for four or more objectives, visualization for extracting above decision-making information gets challenging and new and innovative methods are long overdue. Not only does a trivial visualization becomes difficult, the number of points needed to represent a higher-dimensional front increase exponentially. The existing high-dimensional visualization techniques, such as parallel coordinate plots, scatter plots, RadVis, etc., do not offer a clear and natural view of the Pareto-front in terms of trade-off and other vital localized information needed for a convenient decision-making task. In this paper, we propose a novel way to map a high-dimensional Pareto-front in two and three dimensions. The proposed method tries to capture some of the basic topological properties of the Pareto points and retain them in the mapped lower dimensional space. Therefore, the proposed method can produce faithful representation of the topological primitives of the high-dimensional data points in terms of the basic shape (and structure) of the Pareto-front, its boundary, and visual classification of the relative trade-offs of the solutions. As a proof-of-principle demonstration, we apply our proposed palette visualization method to a few problems.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117325172","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: 2

A Hybrid Deep Learning Model to Predict Business Closure from Reviews and User Attributes Using Sentiment Aligned Topic Model 基于情感一致主题模型从评论和用户属性预测业务关闭的混合深度学习模型

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628823

Sharun S. Thazhackal, V. Devi

引用次数: 2

On Filter Size in Graph Convolutional Networks 图卷积网络中滤波器大小的研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628758

D. V. Tran, Nicoló Navarin, A. Sperduti

引用次数: 45

Learning to Play Pac-Xon with Q-Learning and Two Double Q-Learning Variants 学习玩吃豆棋的Q-Learning和两个双Q-Learning变体

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628782

Jits Schilperoort, Ivar Mak, Mădălina M. Drugan, M. Wiering

引用次数: 9

Multi-objective Metaheuristics for Managing Futures Portfolio Risk 期货投资组合风险管理的多目标元启发式方法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628879

G. Pai

{"title":"Multi-objective Metaheuristics for Managing Futures Portfolio Risk","authors":"G. Pai","doi":"10.1109/SSCI.2018.8628879","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628879","url":null,"abstract":"Trading with futures in the Derivatives financial markets, is fraught with risks. To mitigate the risks, a multipronged approach such as diversification in different asset classes across dissimilar markets or imposing risk budgets on individual assets and/or asset classes or enforcing capital budgets and other investor preferential constraints modeling their risk appetites and allocation limits, needs to be adopted. However, the enforcement of such constraints turns the futures portfolio optimization problem model complex, rendering it difficult for direct solving using traditional methods engendering the need to look for metaheuristic solutions.In this work, we discuss the metaheuristic optimization of a long-only futures portfolio with the objective of maximizing its diversification index, in the face of Risk Budgeting and other investor specific constraints that serve to curtail risk. Adopting Diversification Ratio for its diversification index and enforcing risk budgets on the individual assets as well as on asset classes turns the transformed problem model into a Multi-objective Non-linear Non Convex Constrained Fractional Programming problem, to solve which metaheuristics has been applied. In the absence of reported work for a problem of such a nature and scale, two strategies from two different genres of metaheuristics, viz., Multi-objective Differential Evolution and Multi-objective Evolution Strategy have been evolved to obtain the Pareto Optimal solution set and their results and performances have been compared. Experimental simulations have been undertaken over a futures portfolio of equity indices and bonds spread across global markets, making use of a historical data set for the period March 2004-June 2013.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663915","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: 2

A Computationally Efficient Neural Network For Faster Image Classification 一种计算效率高的快速图像分类神经网络

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628751

Ananya Paul, L. TejpratapG.V.S.

引用次数: 0

Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals 在情绪分析中使用股票价格作为基础真相来产生有利可图的交易信号

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628841

Ellie Birbeck, D. Cliff

{"title":"Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals","authors":"Ellie Birbeck, D. Cliff","doi":"10.1109/SSCI.2018.8628841","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628841","url":null,"abstract":"The increasing availability of “big” (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e., positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826048","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

Simultaneous Localisation and Optimisation for Swarm Robotics 群体机器人的同步定位和优化

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628767

Sebastian Mai, Christoph Steup, Sanaz Mostaghim

引用次数: 2

Design of an Adaptive Push-Repel Operator for Enhancing Convergence in Genetic Algorithms 一种提高遗传算法收敛性的自适应推退算子设计

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628790

Yashesh D. Dhebar, K. Deb

{"title":"Design of an Adaptive Push-Repel Operator for Enhancing Convergence in Genetic Algorithms","authors":"Yashesh D. Dhebar, K. Deb","doi":"10.1109/SSCI.2018.8628790","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628790","url":null,"abstract":"Genetic Algorithms (GAs) are demonstrated to be successful in solving problems pertaining to the field of engineering, physics, medicine, finance and many more. The efficacy of GAs lies in its efficiency at exploring complex design-space with black-box constraints and reach the optimal regions defined by functions of unknown fitness landscapes (or in other words, black-box optimization functions). Depending on the nature of the problem, the design-space can have continuous, discrete or mixed (continuous and discrete) set of design-variables. The exploration in this design-space is conducted through a population of individuals and is primarily driven by three operations –selection, recombination (or crossover) and mutation. The exploitation aspect of a GA search is obtained by its selection operation, while crossover and mutation operations deal with the exploration aspect for generating new solutions in the search space. In this study, an attempt has been made to balance the two aspects by designing a generic push operator which introduces an extra level of exploitation in the algorithm by biasing the creation of solutions near the best-so-far solution. In addition to standard search operators, an additional diversity maintaining repel operator is introduced to balance the exploitation-exploration issue. Simulations are performed to understand the effect of an adaptive push-repel GA on different fitness landscapes for both unconstrained and constrained optimization problems. The results are promising and encourage their extensions to other evolutionary algorithms.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404514","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

Ensemble of Pareto-based Selections for Many-objective optimization 基于pareto选择的多目标优化集成

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628907

Samira Ghorbanpour, Vikas Palakonda, R. Mallipeddi

{"title":"Ensemble of Pareto-based Selections for Many-objective optimization","authors":"Samira Ghorbanpour, Vikas Palakonda, R. Mallipeddi","doi":"10.1109/SSCI.2018.8628907","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628907","url":null,"abstract":"Performance of Pareto Dominance-based Multi-objective Evolutionary Algorithms (PDMOEAs) degrades in many-objective optimization problems (MaOPs), where the number of objectives is greater than three. The degradation in the performance of PDMOEs arises due to the inability of Pareto dominance relationships that are decided using conventional nondominated sorting (CNDS) to differentiate between the population members during environmental selection. Therefore, the selection of individuals depends entirely on the secondary criterion that enforces diversity. In literature, the idea of modifying the definition of Pareto dominance to improve the converging ability of PDMOEAs has been investigated. Recently, an approximate effective nondominated sorting (AENS) was proposed, that utilizes only three objective comparisons to determine the dominance relation between the individuals. PDMOEAs based on the approximation of Pareto dominance improves the convergence, but fails to enforce the diversity; whereas the use of conventional Pareto dominance enforces the necessary diversity but fail to achieve the convergence. In this paper, we propose an ensemble of Pareto-based selections (EPS) to improve the performance of PDMOEAs on many-objective optimization problems. The ensemble includes – a) environmental and mating selections of any existing PDMOEA based on CNDS and respective density estimation; and b) environmental and mating selections based on AENS and shift-based density estimation. Experiments are performed on 16 different test problems with two different PDMOEA frameworks to analyze the performance of proposed ensemble of Pareto-based selections (EPS).","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125616140","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: 2