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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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 languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 25th International Conference, DEXA 2014, Proceedings
EditorsH Decker, L Lhotska, S Link, M Spies, R.R Wagner
PublisherSpringer-Verlag Italia
Pages147-154
Number of pages8
EditionPART 2
ISBN (Electronic)9783319100852
ISBN (Print)9783319100845
DOIs
Publication statusPublished - 1 Jan 2014
Event25th International Conference on Database and Expert Systems Applications, DEXA 2014 - Munich, Germany
Duration: 1 Sep 20144 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8645 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Database and Expert Systems Applications, DEXA 2014
CountryGermany
CityMunich
Period1/09/144/09/14

Fingerprint

Synergy
Initialization
Genetic algorithms
Genetic Algorithm
Process Optimization
Speedup
Calculate
Experiments
Experiment

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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 proceedingConference proceeding

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). https://doi.org/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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