Negative selection(NS) algorithm is the core algorithm of artificial immune system, in which the detector generate
mechanism is the key. But the performance of V-detector algorithm becomes unfavorable on high-dimension data and the
set of initial detectors randomly generated are too concentrated leading to the algorithm convergence prematurely. Quasi
random sequence is used to generate the set of initial detectors. Then the detector set is optimized by using clone selection,
and the coverage of non-self-space and the number of detectors are used as the standard of affinity which can over come the
limitations of ENSA. A new selection, cloning and mutation operator is used to generate the optimal mature detector set.
Finally, experiments verify the effectiveness of the proposed algorithm.