2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)最新文献

Heuristic generation via parameter tuning for online bin packing 基于参数调整的启发式在线装箱算法

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-12 DOI: 10.1109/EALS.2014.7009510

Ahmet Yarimcam, Shahriar Asta, E. Özcan, A. Parkes

引用次数: 11

RTSDE: Recursive total-sum-distances-based density estimation approach and its application for autonomous real-time video analytics 基于总和距离的递归密度估计方法及其在自主实时视频分析中的应用

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-09 DOI: 10.1109/EALS.2014.7009507

P. Angelov, A. Wilding

引用次数: 2

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex HyFlex中用于车辆路由的学徒学习超启发式算法

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-09 DOI: 10.1109/EALS.2014.7009505

Shahriar Asta, E. Özcan

{"title":"An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex","authors":"Shahriar Asta, E. Özcan","doi":"10.1109/EALS.2014.7009505","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009505","url":null,"abstract":"Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. In this study, we investigate into a novel apprenticeship-learning-based approach which is used to automatically generate a hyper-heuristic for vehicle routing. This approach itself can be considered as a hyper-heuristic which operates in a train and test fashion. A state-of-the-art hyper-heuristic is chosen as an expert which is the winner of a previous hyper-heuristic competition. Trained on small vehicle routing instances, the learning approach yields various classifiers, each capturing different actions that the expert hyper-heuristic performs during the search process. Those classifiers are then used to produce a hyper-heuristic which is potentially capable of generalizing the actions of the expert hyper-heuristic while solving the unseen instances. The experimental results on vehicle routing using the Hyper-heuristic Flexible (HyFlex) framework shows that the apprenticeship-learning-based hyper-heuristic delivers an outstanding performance when compared to the expert and some other previously proposed hyper-heuristics.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132810859","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: 24

Embodied artificial life at an impasse can evolutionary robotics methods be scaled? 陷入绝境的拟人化人工生命进化机器人方法能否规模化?

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009500

A. Nelson

{"title":"Embodied artificial life at an impasse can evolutionary robotics methods be scaled?","authors":"A. Nelson","doi":"10.1109/EALS.2014.7009500","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009500","url":null,"abstract":"Evolutionary robotics (ER) investigates the application of artificial evolution toward the synthesis of robots capable of performing autonomous behaviors. Over the last 25 years, researchers have reported increasingly complex evolved behaviors, and have compiled a de facto set of benchmark tasks. Perhaps the best known of these is the obstacle avoidance and target homing task performed by differential drive robots. More complex tasks studied in recent ER work include augmented variants of the rodent T-maze and complex foraging tasks. But can proof-of-concept results such as these be extended to evolve complex autonomous behaviors in a general sense? In this topical analysis paper we survey relevant research and make the case that common tasks used to demonstrate the effectiveness of evolutionary robotics are not characteristic of more general cases and in fact do not fully prove the concept that artificial evolution can be used to evolve sophisticated autonomous agent behaviors. Robots capable of performing many of the tasks studied in ER have now been evolved using nearly aggregate binary success/fail fitness functions. However, arguments used to support the necessity of incremental methods for complex tasks are essentially sound. This raises the possibility that the tasks themselves allow for relatively simple solutions, or span a relatively small candidate solution set. This paper presents these arguments in detail and concludes with a discussion of current ER research.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357791","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: 3

Influence of the data codification when applying evolving classifiers to develop spoken dialog systems 应用进化分类器开发口语对话系统时数据编码的影响

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009504

J. A. Iglesias, D. Griol, Agapito Ledezma, A. Sanchis

{"title":"Influence of the data codification when applying evolving classifiers to develop spoken dialog systems","authors":"J. A. Iglesias, D. Griol, Agapito Ledezma, A. Sanchis","doi":"10.1109/EALS.2014.7009504","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009504","url":null,"abstract":"In this paper we present a study of the influence of the representation of the data when applying evolving classifiers in a specific classification task. In particular, we consider an evolving classifier for the development of a spoken dialog system interacting in a practical domain. In order to conduct this study, we will first introduce an approach based on evolving fuzzy systems (EFS) which is employed to select the next system action of the dialog system. This classifier takes into account a set of evolving fuzzy rules which are automatically obtained using evolving systems. The reason for using EFS in this domain is that we can process streaming data on-line in real time and the structure and operation of the dialog model can dynamically change by considering the interaction of the dialog system with its users. Since we want to apply this evolving approach in a real domain, our proposal considers the data supplied by the user throughout the complete dialog history and the confidence measures provided by the recognition and understanding modules of the system. The paper is focused on the study of the influence of the codification of this input data to achieve the best performance of the proposed approach. To do this, we have completed this study for a real spoken dialog system providing railway information.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134214334","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

Recursive possibilistic fuzzy modeling 递归可能性模糊建模

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009498

Leandro Maciel, F. Gomide, R. Ballini

引用次数: 16

Real time road traffic monitoring alert based on incremental learning from tweets 基于推文增量学习的实时道路交通监测警报

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009503

Di Wang, Ahmad Al-Rubaie, John Davies, Sandra Stincic

引用次数: 20

A fully autonomous Data Density based Clustering technique 一个完全自主的基于数据密度的聚类技术

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009512

Richard Hyde, P. Angelov

{"title":"A fully autonomous Data Density based Clustering technique","authors":"Richard Hyde, P. Angelov","doi":"10.1109/EALS.2014.7009512","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009512","url":null,"abstract":"A recently introduced data density based approach to clustering, known as Data Density based Clustering has been presented which automatically determines the number of clusters. By using the Recursive Density Estimation for each point the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use. The Data Density based Clustering method however requires an initial cluster radius to be entered. A different radius per feature/ dimension creates hyperellipsoid clusters which are axis-orthogonal. This results in a greater differentiation between clusters where the clusters are highly asymmetrical. In this paper we update the DDC method to automatically derive suitable initial radii. The selection is data driven and requires no user input. We compare the performance of DDCAR with DDC and other standard clustering techniques by comparing the results across a selection of standard datasets and test datasets designed to test the abilities of the technique. By automatically estimating the initial radii we show that we can effectively cluster data with no user input. The results demonstrate the validity of the proposed approach as an autonomous, data driven clustering technique. We also demonstrate the speed and accuracy of the method on large datasets.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309872","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: 12

Distributed GAs with case-based initial populations for real-time solution of combinatorial problems 基于实例初始种群的组合问题实时解的分布式遗传算法

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009509

Takashi Kawabe, Masaki Suzuki, Taro Matsumaru, Yukiko Yamamoto, S. Tsuruta, Yoshitaka Sakurai, R. Knauf

引用次数: 3

Anomaly detection based on eccentricity analysis 基于偏心分析的异常检测

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009497

P. Angelov

{"title":"Anomaly detection based on eccentricity analysis","authors":"P. Angelov","doi":"10.1109/EALS.2014.7009497","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009497","url":null,"abstract":"In this paper, we propose a new eccentricity- based anomaly detection principle and algorithm. It is based on a further development of the recently introduced data analytics framework (TEDA - from typicality and eccentricity data analytics). We compare TEDA with the traditional statistical approach and prove that TEDA is a generalization of it in regards to the well-known “nσ” analysis (TEDA gives exactly the same result as the traditional “nσ” analysis but it does not require the restrictive prior assumptions that are made for the traditional approach to be in place). Moreover, it offers a non-parametric, closed form analytical descriptions (models of the data distribution) to be extracted from the real data realizations, not to be pre-assumed. In addition to that, for several types of proximity/similarity measures (such as Euclidean, cosine, Mahalonobis) it can be calculated recursively, thus, computationally very efficiently and is suitable for real time and online algorithms. Building on the per data sample, exact information about the data distribution in a closed analytical form, in this paper we propose a new less conservative and more sensitive condition for anomaly detection. It is quite different from the traditional “nσ” type conditions. We demonstrate example where traditional conditions would lead to an increased amount of false negatives or false positives in comparison with the proposed condition. The new condition is intuitive and easy to check for arbitrary data distribution and arbitrary small (but not less than 3) amount of data samples/points. Finally, because the anomaly/novelty/change detection is very important and basic data analysis operation which is in the fundament of such higher level tasks as fault detection, drift detection in data streams, clustering, outliers detection, autonomous video analytics, particle physics, etc. we point to some possible applications which will be the domain of future work.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338763","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: 83