Bell-Curve Genetic Algorithm for Mixed Continuous and Discrete Optimization Problems
نویسندگان
چکیده
This paper is the next installment in a series (Sobieszczanski-Sobieski et al. 1998, Kincaid et al. 2000, 2001, 2002 and Plassman and Sobieszczanski-Sobieski 2000) that has introduced a variant of the Genetic Algorithm in which the reproduction mechanism was modified to base it on the Gaussian probability distribution, the bell curve. The bell-curve based (BCB) heuristic procedure, first presented in Sobieszczanski-Sobieski, Laba, and Kincaid (1998), is similar in spirit to Evolutionary Search strategies (ESs) and Evolutionary Programming methods (EPs) but has fewer parameters to adjust. In Sobieszczanski-Sobieski et al. (1998) BCB was tested on a structural design optimization problem. The quality of solutions generated were verified by comparing BCB solutions to ones generated by a standard nonlinear programming technique. No attempt was made to analyze the sensitiv-
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