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Genetic algorithms(GAs) are adaptable to discrete and constrained problems and do not require continuity, unimodality, and derivatives of function. But GAs have a demerit of late convergence, which means to be examined many individuals until find the best individual. And especially much time is required to compute the constraints of an individual in large scale or nonlinear structural problem. So it needs to be developed the genetic algorithm to converges fast in small population.Although the selected algorithm is superior, it's performance could be very fluctuate depending on search space. And it is possible to vary and adapt the algorithm to a placed circumstance. Therefore it is very important for user to create more efficient algorithm.In this paper, micro genetic algorithm effective in small population size is used to save computing time in size optimization of geometrically nonlinear space truss. But in practical structural size optimization problem, many design variables are needed and search space is very complicated. Also optimum solutions exist at the corner (boundaries between feasible and infeasible regions) of the search space. In this case, micro GAs exist so far are liable to fall into dead corner and some genetic operators of those do not work well because the outcomes of the operator are apt to be infeasible. To cope with these problem it is proposed to reduce the intensity of penalty in order to extend the boundaries of search space and to use the hybrid genetic algolithm by gradient-like-selector to converge fast and avoid the genetic drift as a result of copies.