Improving the effectiveness of information retrieval system under vector space model using adaptive genetic algorithm

Dissertant

Maayitah, Wafa Zaal Muhammad

Thesis advisor

al-Rababiah, Mamun S.
Kanan, Ghassan Jaddu

Comitee Members

al-Nihoud, Jihad Quball Awdah
al-Smadi, Adnan M.
al-Shalabi, Riyad

University

Al albayt University

Faculty

Prince Hussein Bin Abdullah Faculty for Information Technology

Department

Department of Computer Science

University Country

Jordan

Degree

Master

Degree Date

2010

English Abstract

Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic algorithm. Genetic algorithm started to be applied in information retrieval system in order to optimize the query by genetic algorithm, a good query is a set of terms that express accurately the information need while being usable within collection corpus, the last part of this specification is critical for the matching process to be efficient, that is why most research efforts are actually put toward the query improvement. We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean model, and Language model in information retrieval (IR), the algorithm used crossover and mutation operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those, and remain unchanged during execution.

GA is developed to support adaptive adjustment of mutation and crossover probability; this allows faster attainment of better solutions.

The research has been tested using 242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.

Main Subjects

Information Technology and Computer Science

Topics

• Algorithms
• Information retrieval

No. of Pages

55

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-310850