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Simultaneous Selection Of Variables And Smoothing Parameters By Genetic Algorithms
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Book Synopsis Simultaneous Selection of Variables and Smoothing Parameters by Genetic Algorithms by :
Download or read book Simultaneous Selection of Variables and Smoothing Parameters by Genetic Algorithms written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The combinationof these tools often leads to inappropriate results. In this paper we propose a simulataneous choice of variables and smoothing parameters based on genetic algorithms. Common genetic algorithms have to be modified since inclusion of variables and smoothing have to be coded separately but are linked in the search for optimal solutions. The basic tool for fitting the additive model is the penalized expansion in B-splines. -- Genetic algorithm ; Additive model ; Variable selection ; Penalized regression splines ; B-splines ; Improved AIC ; BIC
Book Synopsis Genetic Algorithms as Tool for Statistical Analysis of High-Dimensional Data Structures by : Rüdiger Krause
Download or read book Genetic Algorithms as Tool for Statistical Analysis of High-Dimensional Data Structures written by Rüdiger Krause and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In regression the objective is to determine an appropriate function which reflects reality as accurate as possible but also eliminates irregularities from data noise and is therefore easy to interpret. A popular and flexible approach for estimating the true underlying function is the additive model. One possible approach for fitting additive models is the expansion in B-splines which allows direct calculation of the estimators. If the number of B-splines is too large the estimated functions become wiggly and tend to be very close to the observed data. To avoid this problem of overfitting we use a penalization approach characterized by smoothing parameters. In this thesis we propose the use of genetic algorithms for smoothing parameter optimization. Genetic algorithms are rarely applied in the field of statistics and refer to the principle that better adapted individuals win against their competitors under equal conditions. Apart from smoothing parameter optimization the user often faces datasets containing large numbers of relevant and irrelevant explanatory variables. Appropriate variable selection approaches allow to reduce the number of variables to subsets of relevant variables. We propose to consider the problems of variable selection and choice of smoothing parameters simultaneously by using genetic algorithms. Our approach bases on an appropriate combination of the genetic algorithms for smoothing parameter optimization and variable selection.
Book Synopsis Innovations in Classification, Data Science, and Information Systems by : Daniel Baier
Download or read book Innovations in Classification, Data Science, and Information Systems written by Daniel Baier and published by Springer Science & Business Media. This book was released on 2006-06-06 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.
Book Synopsis Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis by : Shuang Huang
Download or read book Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis written by Shuang Huang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sparse simultaneous penalized variable selection method for data with hierarchical structure is proposed to identify the quantitative trait loci and expression traits that are related to certain clinical trait in genetic data analysis. This method is developed for data sets in which the dependency is linear, and among a large number of gene loci and expression traits candidates, relatively few are important to the interested clinical trait. The method focuses on identifying the candidates in genome set and expression traits that are significantly related to clinical observation via the hierarchical dependence structure. A penalized linear model is used to reduce the number of parameters, using a novel computational algorithm that can handle the unknowns simultaneously. A data-adaptive tuning procedure based on cross validation acts as a parameter selector. Simulation studies are conducted to check the performance of the proposed method, and to compare with some well developed methods, including several penalized methods and Step AIC method. The real data application is done on a data set from an obesity study. The data set contains 541 mice, and for each individual, over 1,000 expression traits and around 1,000 gene loci are recorded. We compare the finding of our method with previous studies on the same species of mice and the similarity and difference of the outcomes are discussed.
Book Synopsis Genetic Algorithms and their Applications by : John J. Grefenstette
Download or read book Genetic Algorithms and their Applications written by John J. Grefenstette and published by Psychology Press. This book was released on 2013-08-21 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: First Published in 1987. This is the collected proceedings of the second International Conference on Genetic Algorithms held at the Massachusetts Institute of Technology, Cambridge, MA on the 28th to the 31st July 1987. With papers on Genetic search theory, Adaptive search operators, representation issues, connectionism and parallelism, credit assignment ad learning, and applications.
Book Synopsis Applied Genetic Algorithm and Its Variants by : Nilanjan Dey
Download or read book Applied Genetic Algorithm and Its Variants written by Nilanjan Dey and published by Springer Nature. This book was released on 2023-07-01 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.
Book Synopsis Parameters Estimation in Double Exponential Smoothing Using Genetic Algorithm by : Fong Yeng Foo
Download or read book Parameters Estimation in Double Exponential Smoothing Using Genetic Algorithm written by Fong Yeng Foo and published by . This book was released on 2010 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Genetic Algorithms in Engineering Systems by : Ali M. S. Zalzala
Download or read book Genetic Algorithms in Engineering Systems written by Ali M. S. Zalzala and published by IET. This book was released on 1997 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions presented in this book are extended version of commissioned papers from some of the highest quality contributions to the conference. Chosen for their experience in the field, the authors are drawn from academia and industry worldwide. The chapters cover the main fields of work as well as presenting tutorial material in this important subject, which is currently receiving considerable attention from engineers.
Book Synopsis Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness by : C. H. M. van Kemenade
Download or read book Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness written by C. H. M. van Kemenade and published by . This book was released on 1995 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "In many Genetic Algorithms applications the objective is to find a (near- )optimal solution using a limited amount of computation. Given these requirements it is difficult to find a good balance between exploration and exploitation. Usually such a balance is found by tuning the various parameters (like the selective pressure, population size, the mutation- and crossover rate) of the Genetic Algorithm. As an alternative we propose simultaneous tuning of the selective pressure and the disruptiveness of the recombination operators. Our experiments show that the combination of a proper selective pressure and a highly disruptive recombination operator yields superior performance. The reduction mechanism used in a Steady-State GA has a strong influence on the optimal crossover disruptiveness. Using the worst fitness deletion strategy the building blocks present in the current best individuals are always preserved. This releases the crossover operator from the burden to maintain good building blocks and allows us to tune crossover disruptiveness to improve the search for better individuals."
Book Synopsis Adaptive and Natural Computing Algorithms by : Ville Kolehmainen
Download or read book Adaptive and Natural Computing Algorithms written by Ville Kolehmainen and published by Springer. This book was released on 2009-09-30 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.
Book Synopsis Introduction to Functional Data Analysis by : Piotr Kokoszka
Download or read book Introduction to Functional Data Analysis written by Piotr Kokoszka and published by CRC Press. This book was released on 2017-09-27 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.
Book Synopsis Optimization in Industry by : Ian Parmee
Download or read book Optimization in Industry written by Ian Parmee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization in Industry comprises a collection of papers presented at the third US United Engineering Foundation's 'Optimization in Industry' Conference. The main thrust of this, the third conference of the series is related to engineering optimization including both manufacture and parametric design. The papers included explore the relationships between well-established deterministic optimization methods and the emerging stochastic and mainly population-based search and optimization algorithms. A mix of approaches across a wide range of engineering disciplines is included. It illustrates the manner in which various techniques can be utilised either in a stand-alone manner or within hybrid systems to give best performance in terms of optimal design and computational efficiency. The papers span scientific, application, awareness/information dissemination and industrial requirements areas. They provide information on available search and optimization techniques and their application to specific design problems and across the field of manufacturing generally. Papers identifying and dealing with problems of incorporating novel optimization techniques within day-to-day design practice and industrial software requirements are also included. The book will thus be of interest to both the industrial and academic communities.
Book Synopsis Systems Approach to Social Engineering. by :
Download or read book Systems Approach to Social Engineering. written by and published by Allied Publishers. This book was released on 1999 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment by : Kunal Roy
Download or read book Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment written by Kunal Roy and published by Academic Press. This book was released on 2015-03-03 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. Includes numerous practical examples related to QSAR methods and applications Follows the Organization for Economic Co-operation and Development principles for QSAR model development Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools
Book Synopsis Performance Analysis for Genetic Algorithms by : Hermrean Wong
Download or read book Performance Analysis for Genetic Algorithms written by Hermrean Wong and published by . This book was released on 1995 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been shown effective for solving complex optimization problems such as job scheduling, machine learning, pattern recognition, and assembly planning. Due to the random process involved in genetic algorithms, the analysis of performance characteristics of genetic algorithms is a challenging research topic. Studied in this dissertation are methods to analyze convergence of genetic algorithms and to investigate whether modifications made to genetic algorithms, such as varying the operator rates during the iterative process, improve their performance. Both statistical analysis, which is used for investigation of different modifications to the genetic algorithm, and probability analysis, which is used to derive the expectation of convergence, are used in the study. The Wilcoxon signed rank test is used to examine the effects of changing parameters in genetic algorithms during the iterations. A Markov chain is derived to show how the random selection process affects the genetic evolution, including the so called genetic drift and preferential selection. A link distance is introduced as a numerical index for the study of the convergence process of order-based genetic algorithms. Also studied are the effects of random selection, mutation operator, and the combination of both to the expected average link distance. The genetic drift is shown to enforce the convergence exponentially with increase in the number of iterations. The mutation operator, on the other hand, suppresses the convergence. The combined results of these two parameters lead to a general formula for the estimation of the expected number of iterations needed to achieve convergence for the order-based genetic algorithm with selection and mutation and provide important insights about how order-based genetic algorithms converge.
Book Synopsis The Practical Handbook of Genetic Algorithms by : Lance D. Chambers
Download or read book The Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2000-12-07 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material
Book Synopsis An Introduction to Genetic Algorithms for Scientists and Engineers by : David A. Coley
Download or read book An Introduction to Genetic Algorithms for Scientists and Engineers written by David A. Coley and published by World Scientific. This book was released on 1999 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book has been designed to be useful to most practising scientists and engineers, whatever their field and however rusty their mathematics and programming might be. The approach taken is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL AND C) included on a floppy disk to help the reader get up and running as quickly as possible. The text could also be used as part of an undergraduate course on search and optimisation. Student exercises are included at the end of several of the chapters, many of which are computer-based and designed to encourage exploration of the method.
