Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference最新文献_第2页

Semantic variation operators for multidimensional genetic programming. 多维遗传规划的语义变异算子。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2019-07-01 DOI: 10.1145/3321707.3321776

William La Cava, Jason H Moore

引用次数: 15

GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis. 与细菌性阴道病相关的基于ga的阴道微生物组特征选择。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2014-01-01 DOI: 10.1145/2576768.2598378

Joi Carter, Daniel Beck, Henry Williams, James Foster, Gerry Dozier

引用次数: 7

Initialization Parameter Sweep in ATHENA: Optimizing Neural Networks for Detecting Gene-Gene Interactions in the Presence of Small Main Effects. 雅典娜初始化参数扫描:优化神经网络检测基因-基因相互作用的存在小主效应。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2010-01-01 DOI: 10.1145/1830483.1830519

Emily R Holzinger, Carrie C Buchanan, Scott M Dudek, Eric C Torstenson, Stephen D Turner, Marylyn D Ritchie

{"title":"Initialization Parameter Sweep in ATHENA: Optimizing Neural Networks for Detecting Gene-Gene Interactions in the Presence of Small Main Effects.","authors":"Emily R Holzinger, Carrie C Buchanan, Scott M Dudek, Eric C Torstenson, Stephen D Turner, Marylyn D Ritchie","doi":"10.1145/1830483.1830519","DOIUrl":"https://doi.org/10.1145/1830483.1830519","url":null,"abstract":"<p><p>Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, gene-gene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multi-locus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.</p>","PeriodicalId":88876,"journal":{"name":"Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference","volume":"12 ","pages":"203-210"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1830483.1830519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29530832","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: 22

Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming. 利用专家知识进行上位性全基因组分析的合理初始化。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2009-05-18 DOI: 10.1109/CEC.2009.4983093

Casey S Greene, Bill C White, Jason H Moore

引用次数: 14

Mask functions for the symbolic modeling of epistasis using genetic programming 使用遗传规划的上位性符号建模的掩码函数

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2008-07-13 DOI: 10.1145/1389095.1389154

R. Urbanowicz, Nate Barney, B. C. White, J. Moore

引用次数: 4

Mask Functions for the Symbolic Modeling of Epistasis Using Genetic Programming. 基于遗传规划的上位性符号建模的掩模函数。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2008-07-12

Ryan J Urbanowicz, Bill C White, Jason H Moore

引用次数: 0

A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance. 在类不平衡的情况下，平衡的准确度适应度函数使语法进化神经网络具有鲁棒性分析。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2008-01-01 DOI: 10.1145/1389095.1389159

Nicholas E Hardison, Theresa J Fanelli, Scott M Dudek, David M Reif, Marylyn D Ritchie, Alison A Motsinger-Reif

引用次数: 4

Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks. 语法进化优化神经网络的替代交叉策略和选择技术。

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference Pub Date : 2006-01-01 DOI: 10.1145/1143997.1144163

Alison A Motsinger, Lance W Hahn, Scott M Dudek, Kelli K Ryckman, Marylyn D Ritchie

{"title":"Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks.","authors":"Alison A Motsinger, Lance W Hahn, Scott M Dudek, Kelli K Ryckman, Marylyn D Ritchie","doi":"10.1145/1143997.1144163","DOIUrl":"10.1145/1143997.1144163","url":null,"abstract":"One of the most difficult challenges in human genetics is the identification and characterization of susceptibility genes for common complex human diseases. The presence of gene-gene and gene-environment interactions comprising the genetic architecture of these diseases presents a substantial statistical challenge. As the field pushes toward genome-wide association studies with hundreds of thousands, or even millions, of variables, the development of novel statistical and computational methods is a necessity. Previously, we introduced a grammatical evolution optimized NN (GENN) to improve upon the trial-and-error process of choosing an optimal architecture for a pure feed-forward back propagation neural network. GENN optimizes the inputs from a large pool of variables, the weights, and the connectivity of the network - including the number of hidden layers and the number of nodes in the hidden layer. Thus, the algorithm automatically generates optimal neural network architecture for a given data set. \u0000 \u0000Like all evolutionary computing algorithms, grammatical evolution relies on evolutionary operators like crossover and selection to learn the best solution for a given dataset. We wanted to understand the effect of fitness proportionate versus ordinal selection schemes, and the effect of standard and novel crossover strategies on the performance of GENN.","PeriodicalId":88876,"journal":{"name":"Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference","volume":"2006 ","pages":"947-948"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1143997.1144163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29127013","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: 13