Statistics and Computing

An Evolutionary Computation Approach to Statistical Procedures Designs and Applications

By: Roberto Baragona, Francesco Battaglia, Irene Poli

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Hardcover | 5 January 2011

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290 Pages

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22.86 x 15.88 x 1.91

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This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Industry Reviews

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"After the introductory Chapter 1, in Chapter 2, a detailed review of evolutionary computation is presented. Chapters 3-7 then discuss applications in regression, time series, design of experiments, outlier detection, and cluster analysis. Evolutionary techniques have been successfully used to solve optimization problems in several challenging applications, consequently this book may be useful to many applied statisticians." (Snigdhansu Chatterjee, Technometrics, Vol. 54 (4), November, 2012)

"The monograph under review is ... to provide a sort of guide through the world of evolutionary computation for optimization problems in statistics and related implementation issues in a variety of applications. It represents a comprehensive reference work for advanced graduate students and researchers working in the rich field at the intersection between statistics, evolutionary computation, and computer science. ... It is self-contained, each chapter is nicely introduced by a summary of the main contents and a comprehensive list of references is provided." (Marcello Sanguineti, Mathematical Reviews, Issue 2012 d)

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Paperback

Published: 25th February 2013

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Introduction	p. 1
Bio-inspired Optimization Methods	p. 1
Topics Organization	p. 4
Evolutionary Computation	p. 5
Introduction	p. 5
Evolutionary Computation Between Artificial Intelligence and Natural Evolution	p. 5
The Contribution of Genetics	p. 8
Evolutionary Computation Methods	p. 10
Essential Properties	p. 10
Evolutionary Programming	p. 14
Evolution Strategies	p. 16
Genetic Algorithms	p. 18
Estimation of Distribution Algorithms	p. 20
Differential Evolution	p. 23
Evolutionary Behavior Algorithms	p. 25
A Simple Example of Evolutionary Computation	p. 27
Properties of Genetic Algorithms	p. 36
Genetic Algorithms as a Paradigm of Evolutionary Computation	p. 36
Evolution of Genetic Algorithms	p. 41
Convergence of Genetic Algorithms	p. 44
Issues in the Implementation of Genetic Algorithms	p. 51
Genetic Algorithms and Random Sampling from a Probability Distribution	p. 56
Evolving Regression Models	p. 63
Introduction	p. 63
Identification	p. 64
Linear Regression	p. 64
Generalized Linear Models	p. 67
Principal Component Analysis	p. 68
Parameter Estimation	p. 69
Regression Models	p. 69
The Logistic Regression Model	p. 70
Independent Component Analysis	p. 74
ICA algorithms	p. 76
Simple GAs for ICA	p. 77
GAs for Nonlinear ICA	p. 83
Time Series Linear and Nonlinear Models	p. 85
Models of Time Series	p. 86
Autoregressive Moving Average Models	p. 88
Identification of ARMA Models by Genetic Algorithms	p. 91
More General Models	p. 95
Nonlinear Models	p. 97
Threshold AR and Double Threshold GARCH Models	p. 97
Exponential Models	p. 100
Piecewise Linear Models	p. 103
Bilinear Models	p. 114
Real Data Applications	p. 116
Artificial Neural Networks	p. 118
Design of Experiments	p. 125
Introduction	p. 125
Experiments and Design of Experiments	p. 126
Randomization, Replication and Blocking	p. 128
Factorial Designs and Response Surface Methodology	p. 129
The Evolutionary Design of Experiments	p. 132
High-Dimensionality Search Space	p. 132
The Evolutionary Approach to Design Experiments	p. 133
The Genetic Algorithm Design (GA-Design)	p. 135
The Evolutionary Model-Based Experimental Design: The Statistical Models in the Evolution	p. 144
The Evolutionary Neural Network Design (ENN-Design)	p. 144
The Model Based Genetic Algorithm Design (MGA-Design)	p. 147
The Evolutionary Bayesian Network Design (EBN-Design)	p. 152
Outliers	p. 159
Outliers in Independent Data	p. 159
Exploratory Data Analysis for Multiple Outliers Detection	p. 160
Genetic Algorithms for Detecting Outliers in an i.i.d. Data Set	p. 162
Outliers in Time Series	p. 167
Univariate ARIMA Models	p. 169
Multivariate Time Series Outlier Models	p. 181
Genetic Algorithms for Multiple Outlier Detection	p. 184
Detecting Multiple Outliers in Univariate Time Series	p. 186
Genetic Algorithms for Detecting Multiple Outliers in Multivariate Time Series	p. 187
An Example of Application to Real Data	p. 19l
Cluster Analysis	p. 199
The Partitioning Problem	p. 199
Classification	p. 200
Algorithms for Clustering Data	p. 204
Indexes of Cluster Validity	p. 212
Genetic Clustering Algorithms	p. 219
A Genetic Divisive Algorithm	p. 219
Quick Partition Genetic Algorithms	p. 221
Centroid Evolution Algorithms	p. 227
The Grouping Genetic Algorithm	p. 230
Genetic Clustering of Large Data Sets	p. 233
Fuzzy Partition	p. 234
The Fuzzy c-Means Algorithm	p. 234
Genetic Fuzzy Partition Algorithms	p. 236
Multivariate Mixture Models Estimation by Evolutionary Computing	p. 239
Genetic Multivariate Mixture Model Estimates	p. 240
Hybrid Genetic Algorithms and the EM Algorithm	p. 244
Multivariate Mixture Model Estimates with Unknown Number of Mixtures	p. 246
Genetic Algorithms in Classification and Regression Trees Models	p. 248
Clusters of Time Series and Directional Data	p. 248
GAs-Based Methods for Clustering Time Series Data	p. 249
GAs-Based Methods for Clustering Directional Data	p. 254
Multiobjective Genetic Clustering	p. 258
Pareto Optimality	p. 258
Multiobjective Genetic Clustering Outline	p. 259
References	p. 261
Index	p. 273
Table of Contents provided by Ingram. All Rights Reserved.

Statistics and Computing

An Evolutionary Computation Approach to Statistical Procedures Designs and Applications

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Industry Reviews

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