2018 IEEE Congress on Evolutionary Computation (CEC)最新文献

Automatic Evolution of AutoEncoders for Compressed Representations 压缩表示的自动编码器的自动进化

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-08 DOI: 10.1109/CEC.2018.8477874

Filipe Assunção, David Sereno, Nuno Lourenço, P. Machado, B. Ribeiro

{"title":"Automatic Evolution of AutoEncoders for Compressed Representations","authors":"Filipe Assunção, David Sereno, Nuno Lourenço, P. Machado, B. Ribeiro","doi":"10.1109/CEC.2018.8477874","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477874","url":null,"abstract":"Developing learning systems is challenging in many ways: often there is the need to optimise the learning algorithm structure and parameters, and it is necessary to decide which is the best data representation to use, i.e., we usually have to design features and select the most representative and useful ones. In this work we focus on the later and investigate whether or not it is possible to obtain good performances with compressed versions of the original data, possibly reducing the learning time. The process of compressing the data, i.e., reducing its dimensionality, is typically conducted by someone who has domain knowledge and expertise, and engineers features in a trial-and-error endless cycle. Our goal is to achieve such compressed versions automatically; for that, we use an Evolutionary Algorithm to generate the structure of AutoEncoders. Instead of targeting the reconstruction of the images, we focus on the reconstruction of the mean signal of each class, and therefore the goal is to acquire the most representative characteristics of each class. Results on the MNIST dataset show that the proposed approach can not only reduce the original dataset dimensionality, but the performance of the classifiers over the compressed representation is superior to the performance on the original uncompressed images.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991528","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

Landscape-Based Differential Evolution for Constrained Optimization Problems 基于景观的差分进化约束优化问题

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-08 DOI: 10.1109/CEC.2018.8477900

Karam M. Sallam, S. Elsayed, R. Sarker, D. Essam

引用次数: 13

A Novel Approach for Optimizing Ensemble Components in Rainfall Prediction 优化降雨预测中集合成分的新方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-08 DOI: 10.1109/CEC.2018.8477739

Ali Haidar, B. Verma, Toshi Sinha

引用次数: 4

A Multi-Objective Hybrid Filter-Wrapper Evolutionary Approach for Feature Construction on High-Dimensional Data 高维数据特征构建的多目标混合滤波-包装进化方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477771

Marwa Hammami, Slim Bechikh, C. Hung, L. B. Said

{"title":"A Multi-Objective Hybrid Filter-Wrapper Evolutionary Approach for Feature Construction on High-Dimensional Data","authors":"Marwa Hammami, Slim Bechikh, C. Hung, L. B. Said","doi":"10.1109/CEC.2018.8477771","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477771","url":null,"abstract":"Feature selection and construction are important pre-processing techniques in data mining. They may allow not only dimensionality reduction but also classifier accuracy and efficiency improvement. These two techniques are of great importance especially for the case of high-dimensional data. Feature construction for high-dimensional data is still a very challenging topic. This can be explained by the large search space of feature combinations, whose size is a function of the number of features. Recently, researchers have used Genetic Programming (GP) for feature construction and the obtained results were promising. Unfortunately, the wrapper evaluation of each feature subset, where a feature can be constructed by a combination of features, is computationally intensive since such evaluation requires running the classifier on the data sets. Motivated by this observation, we propose, in this paper, a hybrid multiobjective evolutionary approach for efficient feature construction and selection. Our approach uses two filter objectives and one wrapper objective corresponding to the accuracy. In fact, the whole population is evaluated using two filter objectives. However, only non-dominated (best) feature subsets are improved using an indicator-based local search that optimizes the three objectives simultaneously. Our approach has been assessed on six high-dimensional datasets and compared with two existing prominent GP approaches, using three different classifiers for accuracy evaluation. Based on the obtained results, our approach is shown to provide competitive and better results compared with two competitor GP algorithms tested in this study.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706694","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: 6

Surprising Strategies Obtained by Stochastic Optimization in Partially Observable Games 部分可观察对策随机优化的惊奇策略

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477919

M. Cauwet, O. Teytaud

{"title":"Surprising Strategies Obtained by Stochastic Optimization in Partially Observable Games","authors":"M. Cauwet, O. Teytaud","doi":"10.1109/CEC.2018.8477919","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477919","url":null,"abstract":"This paper studies the optimization of strategies in the context of possibly randomized two players zero-sum games with incomplete information. We compare 5 algorithms for tuning the parameters of strategies over a benchmark of 12 games. A first evolutionary approach consists in designing a highly randomized opponent (called naive opponent) and optimizing the parametric strategy against it; a second one is optimizing iteratively the strategy, i.e. constructing a sequence of strategies starting from the naive one. 2 versions of coevolutions, real and approximate, are also tested as well as a seed method. The coevolution methods were performing well, but results were not stable from one game to another. In spite of its simplicity, the seed method, which can be seen as an extremal version of coevolution, works even when nothing else works. Incidentally, these methods brought out some unexpected strategies for some games, such as Batawaf or the game of War, which seem, at first view, purely random games without any structured actions possible for the players or Guess Who, where a dichotomy between the characters seems to be the most reasonable strategy. All source codes of games are written in Matlab/Octave and are freely available for download.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123103110","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

Industrial Portfolio Management for Many-Objective Optimization Algorithms 基于多目标优化算法的工业投资组合管理

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477693

Tobias Rodemann

引用次数: 7

Evolutionary Approach to Straight Line Approximation for Image Matching in Dance-Posture Recognition 基于直线逼近的舞蹈姿态识别图像匹配进化方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477861

P. Rakshit, S. Saha, A. Konar, A. Nagar

引用次数: 1

Texture Representation and Classification with Artificial Hikers and Fractals 基于人工徒步和分形的纹理表示与分类

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477788

L. A. Soares, K. F. Côco, E. Salles, P. M. Ciarelli

引用次数: 1

On the Population Diversity for the Chaotic Differential Evolution 混沌差分进化的种群多样性研究

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477741

R. Šenkeřík, Adam Viktorin, Michal Pluhacek, T. Kadavy

引用次数: 7

SSDP+: A Diverse and More Informative Subgroup Discovery Approach for High Dimensional Data SSDP+:一种针对高维数据的多样化且信息量更大的子群发现方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477855

T. Lucas, Renato Vimieiro, Teresa B Ludermir

引用次数: 2