The BMODS: A powerful 2-D representation scheme for the GA's population

Hari Mohan Pandey, Ankit Chaudhary, Deepti Mehrotra, Zhang Yudong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

Abstract

The genetic algorithm is a search an optimization algorithm has been widely used, need no introduction. There exist various factors such as population size, representation of the population, crossover and mutation probabilities, selection method and others greatly contribute to the success of the genetic algorithm. This paper dealt with the representation of the population for the genetic algorithm. The authors have shown the 2-D representation of the population has been called as bit masking oriented data structure (BMODS) was implemented by Iupsa in 2001. The BMODS is an efficient way to store the individual genome in which reproduction operations have been performed. Recently, the authors have incorporated the BMODS for the grammatical inference system and found encouraging results. By this paper, the aim is to show the usefulness of the BMODS for the representation of the GA's population.

Original languageEnglish
Title of host publicationProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016
EditorsAbhay Bansal, Abhishek Singhal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-52
Number of pages2
ISBN (Electronic)9781467382021, 9781467382038
DOIs
Publication statusE-pub ahead of print - 11 Jul 2016
Event6th International Conference on Cloud System and Big Data Engineering, Confluence 2016 - Uttar Pradesh, Noida, India
Duration: 14 Jan 201615 Jan 2016

Publication series

NameProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016

Conference

Conference6th International Conference on Cloud System and Big Data Engineering, Confluence 2016
Country/TerritoryIndia
CityUttar Pradesh, Noida
Period14/01/1615/01/16

Keywords

  • Crossover
  • Evolutionary Algorithm
  • Genetic algorithm
  • Mutation
  • Optimization
  • Population representation

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