Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F09%3A00020974" target="_blank" >RIV/61989100:27240/09:00020974 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System

Popis výsledku v původním jazyce

With the rapid growth of the amount of data available in electronic libraries, through Internet and enterprise network mediums, advanced methods of search and information retrieval are in demand. Information retrieval systems, designed for storing, maintaining and searching large-scale sets of unstructured documents, are the subject of intensive investigation. An information retrieval system, a sophisticated application managing underlying documentary databases, is at the core of every search engine, including Internet search services. There is a clear demand for fine-tuning the performance of information retrieval systems. One step in optimizing the information retrieval experience is the deployment of Genetic Algorithms, a widely used subclass of Evolutionary Algorithms that have proved to be a successful optimization tool in many areas. In this paper, we revise and extend genetic approaches to information retrieval leverage via the optimization of search queries. As the next trend i

Název v anglickém jazyce

Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System

Popis výsledku anglicky

With the rapid growth of the amount of data available in electronic libraries, through Internet and enterprise network mediums, advanced methods of search and information retrieval are in demand. Information retrieval systems, designed for storing, maintaining and searching large-scale sets of unstructured documents, are the subject of intensive investigation. An information retrieval system, a sophisticated application managing underlying documentary databases, is at the core of every search engine, including Internet search services. There is a clear demand for fine-tuning the performance of information retrieval systems. One step in optimizing the information retrieval experience is the deployment of Genetic Algorithms, a widely used subclass of Evolutionary Algorithms that have proved to be a successful optimization tool in many areas. In this paper, we revise and extend genetic approaches to information retrieval leverage via the optimization of search queries. As the next trend i

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F0990" target="_blank" >GA201/09/0990: Zpracování XML dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Foundations of Computational Intelligence Volume 4

ISBN

978-3-642-01087-3

Počet stran výsledku

26

Strana od-do

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Počet stran knihy

394

Název nakladatele

Springer Heidelberg

Místo vydání

Berlín

Kód UT WoS kapitoly

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