Emerging artificial intelligence techniques such as genetic algorithms (GAs) allow a more realistic representation and solution of difficult and combinatorial problems such as the dynamic traffic queue management problem. Computational experience in solving such complex large-scale problems by use of micro-GAs is described. In addition to providing evidence of the ability of micro-GAs to successfully identify optimal traffic management schemes, some micro-GA-associated computational issues that warrant attention are highlighted. Choosing a proper population size is a critical decision, and internal variability must be accounted for to assess the goodness of the optimization results properly. A simple rule for deciding the best population size for micro-GAs is proposed. Micro-GAs may converge to low-quality solutions, particularly with very small population sizes; convergence of micro-GAs by itself is not a sufficient indication of good performance. The size of the search space for some real-world systems can pose some difficulties to micro-GAs. Choosing between micro-GAs and regular GAs is a problem-dependent decision.


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    Titel :

    Computational Issues in Micro-Genetic Algorithms for Traffic Management


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:


    Erscheinungsdatum :

    1999-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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