Abstract

This paper presents two different Genetic Algorithms (GA) applied to design signal coordination for oversaturated networks. Signal coordination is formulated as a dynamic optimization problem and is solved using GA for the entire duration of congestion. This paper considers a tradeoff between simple GA (SGA), which requires a large population converging in a single convergence epoch, and micro GA (MGA), which requires smaller population with multiple epochs. A comparison is made for given resources available, that is, a fixed number of function evaluations. To provide quality solutions, SGA requires a large population but takes a longer time to converge, and thus it is not efficient for a real-time system. MGA overcomes the drawback encountered by SGA, that is, the time penalty involved in evaluating the fitness values for a large population. This paper reveals that MGA implementation on signal coordination problems reaches the near-optimal region of signal timing much earlier than SGA implementation. For a given number of functional evaluations, a small population size of MGA outperforms SGA executed with a larger population size.

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