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• W2003751475 abstract "This paper presents the applications of steady-state genetic algorithms to discrete optimization of trusses. It is mathematically formulated as a constrained nonlinear optimization problem with discrete design variables. Discrete design variables are treated by a two-stage mapping process which is constructed by the mapping relationships between unsigned decimal integers and discrete values. With small generation gap and careful modification, steady-state genetic algorithms can significantly reduce the computational effort and promote the computational efficiency. The effectiveness, robustness and fast convergence of steady-state genetic algorithms are demonstrated through several examples. The performance of four crossover operators is also compared." @default.
• W2003751475 created "2016-06-24" @default.
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• W2003751475 date "1995-09-01" @default.
• W2003751475 modified "2023-10-03" @default.
• W2003751475 title "Steady-state genetic algorithms for discrete optimization of trusses" @default.
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• W2003751475 doi "https://doi.org/10.1016/0045-7949(94)00551-d" @default.
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