Considering the characteristics of parameter inverse problems on unconfined seepage flow such as high computational cost

numerous objective parameters and wide variation ranges of parameters

and based on the study of parameter inverse problems by means of common genetic algorithm

a new hybrid genetic algorithm is proposed to overcome the limitation of common genetic algorithm such as poor local search ability

premature convergent and excessive computational cost. With water heads used as measured data and seepage coefficient scale genes of different fissured groups selected as objective parameters

a typical slope case study is given. The result shows that the method is effective in identifying the seepage parameters of fissured rock masses on the supposition that the parameter bounds are given. And the method breaks through several limitations of traditional optimization method and improves the efficiency. For the sake of comparisons

the results of the same case study with traditional simplex method used are given.