Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation最新文献

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Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement. 用于蛋白质模型完善的多维缩放和基于 MODELLER 的进化算法。

Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation Pub Date : 2014-07-01 DOI: 10.1109/CEC.2014.6900443

Yan Chen, Yi Shang, Dong Xu

{"title":"Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement.","authors":"Yan Chen, Yi Shang, Dong Xu","doi":"10.1109/CEC.2014.6900443","DOIUrl":"10.1109/CEC.2014.6900443","url":null,"abstract":"Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.","PeriodicalId":89459,"journal":{"name":"Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation","volume":"2014 ","pages":"1038-1045"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380876/pdf/nihms670877.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33193036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification. 结合克隆选择和确定性抽样的高效关联分类。

Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation Pub Date : 2013-01-01 DOI: 10.1109/CEC.2013.6557966

Samir A Mohamed Elsayed, Sanguthevar Rajasekaran, Reda A Ammar

{"title":"Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification.","authors":"Samir A Mohamed Elsayed, Sanguthevar Rajasekaran, Reda A Ammar","doi":"10.1109/CEC.2013.6557966","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557966","url":null,"abstract":"Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, an AC algorithm that integrates the clonal selection of the immune system along with deterministic data sampling. Upon picking a representative sample of the original data, it proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. In addition, the proposed approach is significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.","PeriodicalId":89459,"journal":{"name":"Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation","volume":" ","pages":"3236-3243"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CEC.2013.6557966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32092439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A New Adaptive Framework for Collaborative Filtering Prediction. 一种新的自适应协同过滤预测框架。

Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631164

Ibrahim A Almosallam, Yi Shang

{"title":"A New Adaptive Framework for Collaborative Filtering Prediction.","authors":"Ibrahim A Almosallam, Yi Shang","doi":"10.1109/CEC.2008.4631164","DOIUrl":"10.1109/CEC.2008.4631164","url":null,"abstract":"Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.","PeriodicalId":89459,"journal":{"name":"Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation","volume":"2008 ","pages":"2725-2733"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092383/pdf/nihms115356.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29883725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0