Steady-state genetic algorithms for discrete optimization of trusses

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.