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Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyvaskyla, Finland M. M. Makela, University of Jyvaskyla, Finland P. Neittaanmaki, University of Jyvaskyla, Finland J. Periaux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.

In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for determining quality, there has been no guide to easy learning and implementation until now. Filling that gap, Statistical Design of Experiments with Engineering Applications, provides a ready made, quick and easy-to-learn approach for applying design of experiments techniques to problems. The book uses quality as the main theme to explain various design of experiments concepts. The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques.

Keyword density - Keyword density is the percentage of words on a web page that match a specified set of keywords. In the context of search engine optimization keyword density can be used as a factor in determining whether a web page is relevant to a specified keyword or keyword phrase.

Keyword stuffing - Keyword stuffing is considered to be an unethical Search engine optimization (SEO) technique. Keyword stuffing is when a web page is loaded up with keywords in the Meta tags or on the Web page's content.

Search engine optimization - Search engine optimization (SEO) is a set of methods aimed at improving the ranking of a website in search engine listings. The term also refers to an industry of consultants that carry out optimization projects on behalf of clients' sites.

SEO contest - In SEO (search engine optimization) contests, webmasters compete to rank best on Google for a given (usually nonsense) keyword or keyword combination. They have become an often important method for webmasters to promote their web sites and gain web traffic.

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