Multi agent visual area coverage strategies using queen genetic algorithm

Helman Stern, Yoash Chassidim, Moshe Zofi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Using Genetic Algorithms (GA) for solving NP-hard problems is becoming more and more frequent. This paper presents a use of GA with a new selection approach called the Queen GA. The main idea in this approach is not to select both parents from the entire population, but to create a sub group of better solutions (a Queen), and to use one of its members in each performed crossover. We demonstrate the use of the Queen GA for the problem of moving dynamic observers across a polygonal area in order to maximize Visual Area Coverage for a given time horizon. The Queen GA gives superior results over a GA with different selection methods (i.e. proportion, ranking and tournament) at the 0.01 significance level.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Intelligent Systems and Control
EditorsM.H. Hamza, M.H. Hamza
Pages317-322
Number of pages6
StatePublished - 1 Dec 2003
EventProceedings of the IASTED International Conference on Intelligent Systems and Control - Salzburg, Austria
Duration: 25 Jun 200327 Jun 2003

Publication series

NameProceedings of the IASTED International Conference on Intelligent Systems and Control

Conference

ConferenceProceedings of the IASTED International Conference on Intelligent Systems and Control
Country/TerritoryAustria
CitySalzburg
Period25/06/0327/06/03

Keywords

  • Covering Problems
  • Genetic Algorithms

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Modeling and Simulation
  • Artificial Intelligence

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