2013 13th UK Workshop on Computational Intelligence (UKCI)最新文献_第4页

Reconstructing regulatory networks in Streptomyces using evolutionary algorithms 利用进化算法重建链霉菌的调控网络

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651283

S. Thomas, Yaochu Jin, E. Laing, Colin P. Smith

引用次数: 2

State detection from electromyographic signals towards the control of prosthetic limbs 肌电信号状态检测对假肢控制的影响

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651296

Pamela A. Hardaker, Benjamin N. Passow, D. Elizondo

引用次数: 4

Random projections versus random selection of features for classification of high dimensional data 高维数据分类的随机投影与随机特征选择

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651321

Sachin Mylavarapu, A. Kabán

引用次数: 11

A novel adaptive spiral dynamic algorithm for global optimization 一种新的自适应螺旋动态全局优化算法

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651325

A. Nasir, M. Tokhi, O. Sayidmarie, R. Ismail

{"title":"A novel adaptive spiral dynamic algorithm for global optimization","authors":"A. Nasir, M. Tokhi, O. Sayidmarie, R. Ismail","doi":"10.1109/UKCI.2013.6651325","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651325","url":null,"abstract":"This paper presents a novel adaptive spiral dynamic algorithm for global optimization. Through a spiral model, spiral dynamic algorithm has a balanced exploration and exploitation strategy. Defining suitable value for the radius and displacement in its spiral model may lead the algorithm to converge with high speed. The dynamic step size produced by the model also allows the algorithm to avoid oscillation around the optimum point. However, for high dimension problems, the algorithm may easily get trapped into local optima. This is due to the incorporation of a constant radius and displacement in the model. In order to solve the problem, a novel adaptive formulation is proposed in this paper by varying the radius and displacement of the spiral model. The proposed algorithm is validated with various dimensions of unimodal and multimodal benchmark functions. Furthermore, it is applied to parameter optimization of an autoregressive with exogenous terms dynamic model of a flexible manipulator system. Comparison with the original spiral dynamic algorithm shows that the proposed algorithm has better accuracy. Moreover, the time domain and frequency domain responses of the flexible manipulator model shows that the proposed algorithm outperforms its predecessor algorithm.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129986902","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: 20

Towards a method of identifying the causes of poor user experience on websites 寻找一种方法来识别网站用户体验差的原因

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651314

Robert S. K. Miles, Julie Greensmith, Holger Schnädelbach, J. Garibaldi

引用次数: 3

Multi-modal optimisation using a localised surrogates assisted evolutionary algorithm 使用局部代理辅助进化算法的多模态优化

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-10-31 DOI: 10.1109/UKCI.2013.6651292

J. Fieldsend

{"title":"Multi-modal optimisation using a localised surrogates assisted evolutionary algorithm","authors":"J. Fieldsend","doi":"10.1109/UKCI.2013.6651292","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651292","url":null,"abstract":"There has been a steady growth in interest in niching approaches within the evolutionary computation community, as an increasing number of real world problems are discovered that exhibit multi-modality of varying degrees of intensity (modes). It is often useful to locate and memorise the modes encountered - this is because the optimal decision parameter combinations discovered may not be feasible when moving from a mathematical model emulating the real problem to engineering an actual solution, or the model may be in error in some regions. As such a range of disparate modal solutions is of practical use. This paper investigates the use of a collection of localised surrogate models for niche/mode discovery, and analyses the performance of a novel evolutionary algorithm (EA) which embeds these surrogates into its search process. Results obtained are compared to the published performance of state-of-the-art evolutionary algorithms developed for multi-modal problems. We find that using a collection of localised surrogates not only makes the problem tractable from a model-fitting viewpoint, it also produces competitive results with other EA approaches.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130418371","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

Late acceptance-based selection hyper-heuristics for cross-domain heuristic search 基于延迟接受的超启发式跨域启发式搜索

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-09-01 DOI: 10.1109/UKCI.2013.6651310

Warren G. Jackson, E. Özcan, J. Drake

{"title":"Late acceptance-based selection hyper-heuristics for cross-domain heuristic search","authors":"Warren G. Jackson, E. Özcan, J. Drake","doi":"10.1109/UKCI.2013.6651310","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651310","url":null,"abstract":"Hyper-heuristics are high-level search methodologies used to find solutions to difficult real-world optimisation problems. Hyper-heuristics differ from many traditional optimisation techniques as they operate on a search space of low-level heuristics, rather than directly on a search space of potential solutions. A traditional iterative selection hyper-heuristic relies on two core components, a method for selecting a heuristic to apply at a given point and a method to decide whether or not to accept the result of the heuristic application. Raising the level of generality at which search methods operate is a key goal in hyper-heuristic research. Many existing selection hyper-heuristics make use of complex acceptance criteria which require problem specific expertise in controlling the various parameters. Such hyper-heuristics are often not general enough to be successful in a variety of problem domains. Late Acceptance is a simple yet powerful local search method which has only a single parameter to control. The contributions of this paper are twofold. Firstly, we will test the effect of the set of low-level heuristics on the performance of a simple stochastic selection mechanism within a Late Acceptance hyper-heuristic framework. Secondly, we will introduce a new class of heuristic selection methods based on roulette wheel selection and combine them with Late Acceptance acceptance criteria. The performance of these hyper-heuristics will be compared to a number of methods from the literature over six benchmark problem domains.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012522","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: 25

Interpolating destin features for image classification 插值目标特征用于图像分类

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-09-01 DOI: 10.1109/UKCI.2013.6651319

Yongfeng Zhang, C. Shang, Q. Shen

引用次数: 2

Experimental evaluation of cluster quality measures 聚类质量测度的实验评价

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-09-01 DOI: 10.1109/UKCI.2013.6651311

O. Kirkland, B. Iglesia

{"title":"Experimental evaluation of cluster quality measures","authors":"O. Kirkland, B. Iglesia","doi":"10.1109/UKCI.2013.6651311","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651311","url":null,"abstract":"Selecting a “good” clustering solution is one of the major difficulties in clustering data as there are many possible clustering solutions for a given problem, including solutions that contain varying numbers of clusters. Our objective is to select measures of clustering quality that can be applied in a multi-objective optimisation context. Such measures may represent potentially conflicting objectives but should give rise to the “best” clustering solutions from which the user can select a compromise solution. There exists a wide range of cluster quality measures for assessing the quality of a given clustering solution. We begin by summarise some of these. We then propose an experimental evaluation to capture the robustness of different measures under changing conditions. Our experimental setup includes the creation of a number of synthetic clustering solutions which are then degraded in a systematic manner. We measure how the degradation of each measure correlates with the degradation of the solutions according to an external quality measure evaluation. We consider as good those measures that show good correlation. In this context, measures based upon the concept of connectivity show good performance in comparison to others.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441890","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

X-μ fuzzy association rule method X-μ模糊关联规则方法

2013 13th UK Workshop on Computational Intelligence (UKCI) Pub Date : 2013-09-01 DOI: 10.1109/UKCI.2013.6651299

D. Lewis, T. Martin

引用次数: 6