An Improved Memetic Algorithm for Web Search

Deulkar, Khushali; Narvekar, Meera

doi:10.1016/j.procs.2015.03.083

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The similarity between several snippets is computed and the most similar snippets are returned. Khushali D et al (2015) proposed improved memetic algorithm using hybrid selection strategy with local search for enhancing the results [15].…”

Section: Related Workmentioning

confidence: 99%

Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) System for Web Mining

Weslin*¹,

Devadas²

2019

IJEAT

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Wider web space, the searching of a relevant data is the most curious problem for the common people accessing the web. For retreving the relevant information the user request is given to search engine. The relevant pages combined with irrelevant pages are returned to the user. The proposed work emphasizes an Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) for searching the most appropriate pages when submitting complex queries. Improved Memetic algorithm is the traditional genetic algorithm combined with local search and random selection. In this proposed system Improved Memetic algorithm additionally enhanced with logarithmic weight function for more accuracy. Intelligent Agents are introduced in this IMAEIMA to improve its performance and accuracy by reacting intelligently based on feedback and previous experience. This system helps to retrieve relevant pages from web databases with high precision and recall. The derived architecture reveals greater precision and recall overriding the conventional search algorithms.

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Section: Related Workmentioning

confidence: 99%

Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) System for Web Mining

Weslin*¹,

Devadas²

2019

IJEAT

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“…A novel technique improved memetic algorithm (IMA) is proposed in (Deulkar & Narvekar, 2015) which is basically based on a memetic algorithm (MA) for searching in case of complex queries. A hybrid selection policy is used in the proposed technique in order to improve the search results.…”

Section: Application Of Memetic Algorithms For Query Optimization In mentioning

confidence: 99%

Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

Ali

Kiran

Shahzad

2018

ADCAIJ

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Query optimization; Distributed database; EvolutionaryComputation; Bio-inspired algorithms Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution, such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

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“…They also used the Fuzzy logic technique to improve the performance of accelerated PSO by controlling various parameters. Another approach in this field is proposed by (Deulkar & Narvekar, 2015) who found the capacity of the Improved Memetic Algorithm (IMA) to retrieve the most relevant snippets just for the complex queries. The IMA is based on the hybrid-selection method where the random population is generated with the help of the initial population, and the chromosomes are also the same; however, the order is completely different in both.…”

Section: Introductionmentioning

confidence: 99%

A Firefly Algorithm-Based Approach for Web Query Reformulation

Zeboudj

Belkadi

2022

International Journal of Information Retrieval Research

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A major difficulty in using a web-based information retrieval system is the choice of terms to be used for expressing and processing a query. The user has to examine a lot of data to find the necessary documents or information. The problem that often appears in this situation is that the query is incorrect and does not express those needs. Researchers have come up with various solutions to overcome this problem among them the use of query reformulation. This paper presents an approach called FA-QR based on this technique using the Firefly metaheuristic. This algorithm was applied to frequent itemsets generated by frequent- pattern growth (FP Growth). The algorithmic solution allowed the user to select the best path among all the possible solutions for the initial query. Experimentally, the results demonstrated that our proposed approaches achieved a significant improvement over other different methods on TREC and FIRE datasets.

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An Improved Memetic Algorithm for Web Search

Cited by 7 publications

References 5 publications

Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) System for Web Mining

Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) System for Web Mining

Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

A Firefly Algorithm-Based Approach for Web Query Reformulation

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