Enhancing focused crawling with genetic algorithms

Shokouhi, Milad; Chubak, Pirooz; Raeesy, Z.

doi:10.1109/itcc.2005.145

Cited by 18 publications

(17 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GA are powerful and very efficient search and optimization techniques motivated by the natural selection theory of Darwin [4]. Genetic Algorithms [5] are based on the principle of heredity and evolution which claims "in each generation the stronger individual survives and the weaker dies". Therefore, each new generation would contain stronger (fitter) individuals in contrast to its ancestors.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Design of Web Ranking Module using Genetic Algorithm

Thada¹,

Jaglan²

2014

IJCA

View full text Add to dashboard Cite

Crawling is a process in which web search engines collect data from the web. Focused crawling is a special type of crawling process where crawler look for information related to a predefined topic [1].In this paper a method for finding out the most relevant document among a set of documents for the given set of keyword is presented. Relevance checking is done with the help of Rogers-Tanimoto, MountFord and BaroniUrbani/Buser similarity coefficients. The method uses genetic algorithm to show that the average similarity of documents to the query increases when Probability of mutation is taken as low and Probability of crossover is taken as high. The method does the performance analysis of different similarity coefficients on the same set of documents and applies ranking to the documents whose relevancy is highest among the three coefficients.

show abstract

Section: Genetic Algorithmmentioning

confidence: 99%

Design of Web Ranking Module using Genetic Algorithm

Thada¹,

Jaglan²

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…Genetic Algorithms [3] are based on the principle of heredity and evolution which claims "in each generation the stronger individual survives and the weaker dies". Therefore, each new generation would contain stronger (fitter) individuals in contrast to its ancestors.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem

Thada¹,

Dhaka²

2014

IJCA

View full text Add to dashboard Cite

In this paper we solve the non fractional knapsack problem also known as 0-1 knapsack using genetic algorithm. The usual approaches are greedy method and dynamic programming. Its an optimization problem where we try to maximize the values that can be put into a knapsack under the constraint of its weight. We solve the problem using genetic algorithm in matlab using gatool. In this research work different selection schemes have been used like roulette wheel, tournament selection, Stochastic selection etc. Following the introduction of genetic algorithm and knapsack problem, formulation of 0-1 knapsack problem in genetic algorithm is presented. Experimental results using various selection schemes have been analyzed and comparison of genetic algorithm technique is done with greedy method and dynamic programming optimizing techniques.

show abstract

“…The flowchart of the GA-crawler is shown in Figure 2. Although GA-crawler does not add new terms like the Gcrawler [16] and the MultiCrawler Agent (MCA) [17] do, it is expected to maintain a good tracking throughout Web links. In different field, a multi-objective GA for generator contribution based congestion management was proposed by Sen et al [18].…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

“…The selected keywords run as query for three well-known search engines, Google online at http://www.google.com, MSN online at http://www.bing.com, and Yahoo online at http://search.yahoo.com. GA-crawler did not expand initial keywords [16][17], but it could only change the keywords' composition based on the probability of mutation. The crawler is prevented to explore broader search spaces for improving the crawling rate.…”

Section: Step 4 Mutation Based On Meta-searchmentioning

confidence: 99%