• DocumentCode
    1350101
  • Title

    A Process Algebra Genetic Algorithm

  • Author

    Karaman, Sertac ; Shima, Tal ; Frazzoli, Emilio

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
  • Volume
    16
  • Issue
    4
  • fYear
    2012
  • Firstpage
    489
  • Lastpage
    503
  • Abstract
    A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.
  • Keywords
    autonomous aerial vehicles; genetic algorithms; process algebra; assignment problem; chromosomes; crossover; evolutionary based operators; high level mission planning; mission planning; optimization problems; process algebra genetic algorithm; uninhabited aerial vehicles; Algebra; Genetic algorithms; Routing; Schedules; Semantics; Vehicles; Genetic algorithms; UAV task assignment; process algebra;
  • fLanguage
    English
  • Journal_Title
    Evolutionary Computation, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-778X
  • Type

    jour

  • DOI
    10.1109/TEVC.2011.2160400
  • Filename
    6045330