Introduction to Scientific Programming

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Pierpaolo Degano (ed

This book constitutes the refereed proceedings of the 12th European Symposium on Programming, ESOP 2003, held in Warsaw, Poland, in April 2003. The 25 revised full papers presented together with two invited papers were carefully reviewed and selected from 99 submissions. Among the topics addressed are programming paradigms and their integration, program semantics, calculi of computation, security, advanced type systems, program analysis, program transformation, and practical algorithms based on theoretical developments.

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Cyril Fonlupt... [et al.], (eds

The Arti?cial Evolution conference was originally conceived as a forum for the French-speaking Evolutionary Computation community, but has of late been acquiring an European audience, with several papers from Germany, Austria, Italy, Spain... However, AE remains as intended a small and friendly gathering, which will continue to be held every two years. Previous AE meets were held in Toulouse, Brest, and Nˆ ?mes. This year, the hosting was done by the LIL (Laboratoire d’Informatique du Littoral) in the not-so-cold city of Dunkerque. The invited talk on “Fitness Landscapes and Evolutionary Algorithms” was delivered by Colin Reeves of Coventry University This volume contains a selection of the papers presented at the conference. Twenty-seven papers were presented orally at the conference, selected from over 40 papers refereed by the program committee. After the conference, each p- sentation was reviewed and 20 papers were retained and revised for publication in this volume. The papers in this volume have been grouped into the following ?ve sections which more or less re?ect the organization of the oral presentations. 1. Invited Paper: C. Reeves brightly describes the state of the art in Fitness Landscapes.

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Sebastian Thrun, Lorien Pratt

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

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S.N. Sivanandam, S.N. Deepa

"This book is designed to provide an in-depth knowledge on the basic operational features and characteristics of Genetic Algorithms. The various operators and techniques given in the book are pertinent to carry out Genetic Algorithm Research Projects. The book also explores the different types are Genetic Algorithms available with their importance. Implementation of Genetic Algorithm concept has been performed using the universal language C/C++ and the discussion also extends to Genetic Algorithm MATLAB Toolbox. Few Genetic Algorithm problems are programmed using MATLAB and the simulated results are given for the ready reference of the reader. The applications of Genetic Algorithms in Machine learning, Mechanical Engineering, Electrical Engineering, Civil Engineering, Data Mining, Image Processing, and VLSI are dealt to make the readers understand where the concept can be applied."--Jacket.