A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance

Muhammad Marwan Muhammad Fuad

doi:10.1007/978-3-319-10085-2_12

A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance

Muhammad Marwan Muhammad Fuad

Narvik University College

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

In a previous work we presented the ∑-gram distance that computes the similarity between two sequences. This distance includes parameters that we calculated by means of an optimization process using artificial bee colony. In another work we showed how population-based bio-inspired algorithms can be sped up by applying a method that utilizes a pre-initialization stage to yield an optimal initial population. In this paper we use this pre-initialization method on the artificial bee colony algorithm to calculate the parameters of the ∑-gram distance. We show through experiments how this pre-initialization method can substantially speed up the optimization process.

Original language	English
Title of host publication	Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings
Editors	H Decker, L Lhotska, S Link, M Spies, R.R Wagner
Publisher	Springer-Verlag Italia
Pages	147-154
Number of pages	8
Edition	PART 2
ISBN (Electronic)	9783319100852
ISBN (Print)	9783319100845
DOIs	https://doi.org/10.1007/978-3-319-10085-2_12
Publication status	Published - 1 Jan 2014
Event	25th International Conference on Database and Expert Systems Applications, DEXA 2014 - Munich, Germany Duration: 1 Sept 2014 → 4 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8645 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Database and Expert Systems Applications, DEXA 2014
Country/Territory	Germany
City	Munich
Period	1/09/14 → 4/09/14

Keywords

Artificial Bee Colony
Bio-inspired Optimization
Genetic Algorithms
Pre-initialization
∑-gram

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-10085-2_12

Cite this

Muhammad Fuad, M. M. (2014). A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance. In H. Decker, L. Lhotska, S. Link, M. Spies, & R. R. Wagner (Eds.), Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings (PART 2 ed., pp. 147-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8645 LNCS, No. PART 2). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-10085-2_12

A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance. / Muhammad Fuad, Muhammad Marwan.
Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings. ed. / H Decker; L Lhotska; S Link; M Spies; R.R Wagner. PART 2. ed. Springer-Verlag Italia, 2014. p. 147-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8645 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Muhammad Fuad, MM 2014, A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance. in H Decker, L Lhotska, S Link, M Spies & RR Wagner (eds), Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8645 LNCS, Springer-Verlag Italia, pp. 147-154, 25th International Conference on Database and Expert Systems Applications, DEXA 2014, Munich, Germany, 1/09/14. https://doi.org/10.1007/978-3-319-10085-2_12

Muhammad Fuad MM. A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance. In Decker H, Lhotska L, Link S, Spies M, Wagner RR, editors, Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings. PART 2 ed. Springer-Verlag Italia. 2014. p. 147-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-319-10085-2_12

Muhammad Fuad, Muhammad Marwan. / A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance. Database and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings. editor / H Decker ; L Lhotska ; S Link ; M Spies ; R.R Wagner. PART 2. ed. Springer-Verlag Italia, 2014. pp. 147-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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Marwan Fuad

Cite this

A synergy of artificial bee colony and genetic algorithms to determine the parameters of the ∑-gram distance

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Profiles

Marwan Fuad

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