This paper is a literature study on search engine optimization (SEO) considering the leader of the search engine market: Google. There´s an introductory background description of Google and its methods of  crawling the Internet and indexing the web pages, along with a brief review of the famous PageRank algorithm. The purpose of this paper is to describe the major important methods for improved rankings on Google´s result lists. These methods could be categorized as on-page methods tied to the website to be optimized or off-page methods that are external to the website such as link development. Furthermore the most common unethical methods are described, known as ?black hat?, which is the secondary purpose of the text.

This paper is a literature study on search engine optimization (SEO) considering the leader of the search engine market: Google. There´s an introductory background description of Google and its methods of  crawling the Internet and indexing the web pages, along with a brief review of the famous PageRank algorithm. The purpose of this paper is to describe the major important methods for improved rankings on Google´s result lists. These methods could be categorized as on-page methods tied to the website to be optimized or off-page methods that are external to the website such as link development. Furthermore the most common unethical methods are described, known as ?black hat?, which is the secondary purpose of the text.

The purpose of this thesis is to evaluate two pearl growing methods in ISI Web of Science. The methods tested are Find Related Records FRR, an automatic service that uses bibliographic coupling, and Author Keywords AK, where searches are performed using the two first keywords from this field. Both Search methods were used on twenty initial documents and the first twenty hits were evaluated for relevance using a binary scale 0,1. The relevance criteria were based on specific information needs connected to every initial document. The measures used to evaluate the effectiveness of the Search methods were Precision at DCV=10, P10, and Uninterpolated Average Precision AP.

Millions of people are using search engines every day when they are trying to find information on the Internet. The purpose of this report is to find out how people formulate search queries. The result in this report is based on an empirical study which is based on a search log from the Swedish search engine Seek.se.One of the results in this thesis is that nearly all search queries are based on keywords..

The aim of this Master?s thesis is to examine the retrieval performance of two search engines, Yahoo! News and Sesam, specialized on indexing news articles in Swedish. Thirty search questions were used in this study, divided into three different categories: domestic news, amusement and sport. Precision has been calculated both for the 30 search questions as a total and for the three categories as well. Precision was calculated at every DCV-level and then divided with the number of search questions used, in an attempt to reach an average measure.

The theoretical focus in this master is information retrieval (IR), and especially the part of IR that includes interactivity. This research is by some researchers called IIR. Our principal aim was to examine if search engines on the WWW are taking advantage of research results from IIR research. A way to do this is to look at the application of special features, in this case query expansion (QE) and relevance feedback (RF). Both these methods aim for a better matching of documents and the searchers information need.

The purpose of this essay is to perform an evaluation regarding retrieval performance of three web search engines. The search engines included are All the Web, Alta Vista and Google. These have been tested using ten queries within different subject areas chosen by the author. The first twenty hits for each question have been analyzed for relevancy. Relevancy is judged on a three-point scale; 0, 0,5 and 1 where 0 goes to irrelevant and inactive or duplicate documents, 0,5 point goes to partially relevant documents and 1 point goes to highly relevant documents.

The purpose of this thesis is to analyse studies that evaluate Web search engines. This is done in four categories; the researchers? purpose, the evaluation measurements, the relevance, and the time aspect. Our method is based on a text analysis in which we use the direction of analysis of the content of sixteen evaluation experiments. Our results indicate fundamental differences in the way the researchers are tackling the problem of evaluation of Web search engines.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The purpose of this paper has been to identify which modern search functions user of Smartphones are using, why just these functions are being used, and how they have affected the way users acquire information. Modern search functions in this paper means searches that are not based on text, i.e. ways to search for information by other means than writing something into a search field. To identify this, two studies were conducted, one with a questionnaire being distributed with the help of Google Docs through social networks, and one with four interviews with users of modern search functions for Smartphones. The study showed that even though 76 % of Smartphone users are aware of at least one modern search function, only 29% uses one on a regular basis.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restartinga local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA.