SOLVING SIGNAL COORDINATION PROBLEMS USING MASTER-SLAVE GENETIC ALGORITHMS

This paper presents the design of master-slave genetic algorithms (GA) in solving signal coordination problems. When a serial GA is applied its performance in terms of computation time diminishes as more accurate results (smaller time slices to evaluate flows and queues) of network performances are needed, or the size of signal networks increases. Because GA works with a population of independent solutions, it is easy to distribute the computational load, i.e. calculating fitness values of candidate solutions, among several processors and, thus, considerably speed-up the computation time. With a master-slave GA, a single processor (master) performs all genetic operations while a number of processors (slaves) are assigned to evaluate a set of fitness functions. The fundamental step in designing a master-slave GA is to determine the optimal number of processors. In this paper, the analytical formulation of defining the optimal number of processors and the empirical results from the master-slave GA application are presented. A master-slave GA is implemented to a signal coordination problem for a network with oversaturated intersections. For a given network size, the performance of a master- slave GA is investigated when network performances (flows and queues) are evaluated at different sample times. When the fitness evaluation time is large relative to the communication time, the master-slave GA is more efficient and provides larger speed-up

  • Supplemental Notes:
    • Publication Date: 2003. Transportation Research Board, Washington DC. Remarks: Paper prepared for presentation at the 82nd annual meeting of the Transportation Research Board, Washington, D.C., January 2003. Format: CD ROM
  • Corporate Authors:

    University of California, Berkeley

    California PATH Program, Institute of Transportation Studies
    Richmond Field Station, 1357 South 46th Street
    Richmond, CA  United States  94804-4648

    California Department of Transportation

    1120 N Street
    Sacramento, CA  United States  95814

    University of California, Berkeley

    Department of Electrical Engineering and Computer Sciences
    Berkeley, CA  United States  94720
  • Authors:
    • Girianna, Montty
    • Benekohal, Rahim F
  • Conference:
  • Date: 2003

Language

  • English

Media Info

  • Pagination: 22 p.

Subject/Index Terms

Filing Info

  • Accession Number: 00962511
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: PATH, STATEDOT
  • Created Date: Sep 2 2003 12:00AM