Genetic Programming Theory and Practice V

Researchers and practitioners alike are increasingly turning to search, op­ timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv­ ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub­ lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen­ tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub­ lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu­ tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be­ tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface. Part I. Computational Statistics -- I.1. Computational Statistics: An Introduction -- Part Ii: Statistical Computing -- Ii.1. Basic Computational Algorithms -- Ii.2. Random Number Generation -- Ii.3. Markov Chain Monte Carlo Technology -- Ii.4. Numerical Linear Algebra -- Ii.5. The Em Algorithm -- Ii.6. Stochastic Optimization -- Ii.7. Transforms In Statistics -- Ii.8. Parallel Computing Technologies -- Ii.9. Statistical Databases -- Ii.10. Interactive And Dynamic Graphics -- Ii.11. The Grammar Of Graphics -- Ii.12. Statistical User Interfaces -- Ii.13. Object Oriented Computing -- Part Iii: Statistical Methodology -- Iii.1. Model Selection -- Iii.2. Bootstrap And Resampling -- Iii.3. Design And Analysis Of Monte Carlo Experiments -- Iii.4. Multivariate Density Estimation And Visualization -- Iii.5. Smoothing: Local Regression Techniques -- Iii.6. Dimension Reduction Methods -- Iii.7. Generalized Linear Models -- Iii.8. (non) Linear Regression Modeling -- Iii.9. Robust Statistics -- Iii.10. Semiparametic Models -- Iii. 11. Bayesian Computational Methods -- Iii.12. Computational Methods In Survival Analysis -- Iii.13. Data And Knowledge Mining -- Iii.14. Recursive Partitioning And Tree-based Methods -- Iii.15. Support Vector Machines -- Iii. 16. Bagging, Boosting And Ensemble Methods -- Part Iv: Selected Applications -- Iv.1. Computational Intensive Value At Risk Calculations -- Iv.2. Econometrics -- Iv.3. Statistical And Computational Geometry Of Biomolecular Structure -- Iv.4. Functional Magnetic Resonance Imaging -- Iv.5. Network Intrusion Detection. James E. Gentle, Wolfgang Härdle, Yuichi Mori, Editors. Includes Bibliographical References And Index. The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter.

For additional information and supplementary teaching materials, please visit the authors' website at cs.cinvestav.mx/~EVOCINV/bookinfo.html.

The "Handbook of Computational Statistics - Concepts and Methodology" ist divided into 4 parts. It begins with an overview over the field of Computational Statistics, how it emerged as a seperate discipline, how it developed along the development of hard- and software including a discussionof current active research. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focusses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefullness of computational statistics. TOC:Computational Statistics.- Statistical Computing.- Statistical Methodology.- Selected Applications The Handbook of Computational Statistics: Concepts and Methodology is divided into four parts. It begins with an overview over the field of Computational Statistics. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics. Esta obra enfatiza la necesidad de algoritmos numéricos, plantea alguna de las metodologías básicas para la transformación, matenimiento y tratamiento gráfico de bases de datos; también enfatiza la metodología estadística em procedimientos de simulación y visualización de datos multivariables y muestra la aplicación de la estadistica computacional en campos como la Bioinformática, las finanzas y la radiología Beginning with an overview over the field of Computational Statistics, this title presents several topics in the supporting field of statistical computing. It emphasises on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment.