TY - GEN

T1 - A comparison between SGA and BOA applications to design signal coordination

AU - Girianna, Montty

AU - Benekohal, Rahim F.

PY - 2002

Y1 - 2002

N2 - This paper presents the comparison of Genetic Algorithms (GA) application to signal coordination for congested networks. Signal coordination is formulated as a dynamic optimization problem in which green times are decision variables and are represented in the individual GA candidate solutions. Two different types of serial GAs will be used depending on the way a set of candidate solutions is derived. The first is the Simple Genetic Algorithm (SGA). This GA uses three standard genetic operators: selection, crossover, and mutation, to generate a new set of solutions from the previous one. The second is the Bayesian Optimization Algorithm (BOA), which generates new candidate solutions using an estimate of the joint distribution of current promising solutions. The joint distribution is constructed using Bayesian networks. Investigation of each type of GA was made in terms of the number of functional evaluations needed to achieve predefined convergent criteria when the size of GA's candidate solution (population) is increased. Solution qualities are also compared. The growth of functional evaluations for SGA is close to a quadratic function with respect to the size of population, while that for BOA is a cubical function. For a small population size, SGA provides better quality solutions, while for a larger population size, BOA yields better results.

AB - This paper presents the comparison of Genetic Algorithms (GA) application to signal coordination for congested networks. Signal coordination is formulated as a dynamic optimization problem in which green times are decision variables and are represented in the individual GA candidate solutions. Two different types of serial GAs will be used depending on the way a set of candidate solutions is derived. The first is the Simple Genetic Algorithm (SGA). This GA uses three standard genetic operators: selection, crossover, and mutation, to generate a new set of solutions from the previous one. The second is the Bayesian Optimization Algorithm (BOA), which generates new candidate solutions using an estimate of the joint distribution of current promising solutions. The joint distribution is constructed using Bayesian networks. Investigation of each type of GA was made in terms of the number of functional evaluations needed to achieve predefined convergent criteria when the size of GA's candidate solution (population) is increased. Solution qualities are also compared. The growth of functional evaluations for SGA is close to a quadratic function with respect to the size of population, while that for BOA is a cubical function. For a small population size, SGA provides better quality solutions, while for a larger population size, BOA yields better results.

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U2 - 10.1061/40632(245)97

DO - 10.1061/40632(245)97

M3 - Conference contribution

AN - SCOPUS:0036050965

SN - 0784406324

SN - 9780784406328

T3 - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering

SP - 770

EP - 777

BT - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering

PB - ASCE - American Society of Civil Engineers

T2 - Proceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation

Y2 - 5 August 2002 through 7 August 2002

ER -