Open Access Subscription Access
Exploiting the Local Optima in Genetic Algorithm using Tabu Search
Objectives: To explores the process of selecting retrieval schemes along with their weights, and fusion function for data fusion in information retrieval. Methods/Statistical Analysis: This has been carried out using the hybrid Genetic Algorithm. The fusion function, retrieval schemes and their weights lead to a tremendous combination. Finding an optimal solution from this great combination is entirely based on the exploration. Findings: We used, odd and even point crossover as an exploration tool. This exploration tool suffers a setback of slow convergence. The convergence rate can be improved by merging Tabu search, a best local search, with the genetic algorithm. This Tabu GA is used to select the retrieval schemes, weights and fusion function. The outcome of the experiments conducted over the test data sets namely: 1. adi, 2. cisi, and 3. cranlooks promising. We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved. Application/Improvements: We achieved 6.89% of improvement in performance, and the significance of the result is tested statistically. The convergence rate is also improved.
Genetic Algorithm, Information Retrieval, Odd and Even Point Crossover, Tabu GA, Tabu Search
- Salton G, McGill MJ. Introduction to modern Information Retrieval. McGraw-Hill; 1983. p. 1–448.
- Yates RB, Neto BR. Modern Information Retrieval. Addison-Wesley; 1999. p. 1–103. PMid: 10188590.
- Korfhage RR. Information storage and Retrieval. Willey computer Publishing; 1997. p. 1–349.
- ZobelJ, Moffat A. Exploring the similarity space, ACM SIGIR Forum. 1998; 32(1):18–34.
- Lee JH. Combining Multiple Evidence from Different Properties of weighting schemes. Date accessed: 13/07/1995. https://dl.acm.org/citation.cfm?id=215358.
- Lee JH. Combining Multiple Evidence from different relevance feedback network, Database Systems for Advanced Applications. 1997; 97:421–30.
- Billhart H. Learning retrieval expert combinations with genetic algorithms, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2003; 11(3):87– 113. https://doi.org/10.1142/S0218488503001965.
- Fisher HL, Elchesen DR. Effectiveness of combining title words and index terms in machine retrieval searches; Journal of Nature.1972; 109–10.
- Lee JH. Analyses of Multiple Evidence Combination. Proceeding of the ACM SIGIR Conference on Research and Development in Information Retrieval; 1997. 267–76. https://doi.org/10.1145/258525.258587.
- Fox EA, Shaw JA. Combination of multiple searches, Proceeding of the Second Text Retrieval Conference (TREC-2). 1994; 500-215:243–52.
- Fox EA, Shaw JA. Combination of Multiple Searches. Proceeding of the Third Text Retrieval Conference (TREC-3); 1995. p.105–08.
- Gold Berge DE. Genetic Algorithms in Search, Optimization, and Machine learning. Addison-Wesley; 1989. p. 1–432.
- Information Retrieval: A Survey. Date accessed: 30/11/2000. https://www.csee.umbc.edu/csee/research/cadip/readings/ IR.report.120600.book.pdf.
- Chor B, Goldreich O, Kushilevitz E, Sudan M. Private Information Retrieval, Journal of the ACM. 1998; 45(6):965–82. https://doi.org/10.1145/293347.293350.
- Data Fusion. Date accessed: 15/01/2009. https://dl.acm.org/citation.cfm?id=1456651.
- Tabu Search. Date accessed: 2011. https://wiki.eecs.yorku.ca/course_archive/201112/F/4403/_media/tabu_search.pdf.
- Tabu Search Fundamentals and Uses. Date accessed: 1995. https://www.researchgate.net/publication/249776329_ Tabu_Search_Fundamentals_and_Uses.
- Senaratna NI. Genetic Algorithm: The crossover-Mutation Debate. A literature survey (CSS3137-B) submitted in partial fulfillment of the requirements for the Degree of Bachelor of Computer Science (Special) of the University of Colombo; 2005. p. 1–26.
Abstract Views: 391
PDF Views: 0