  In this thesis, the uses of Artificial Immune Systems (AIS) in Machine
learning is studded. the thesis focus on some of immune inspired algorithms
such as clonal selection algorithm and artificial immune network. The effect of
changing the algorithm parameter on its performance is studded. Then a new
immune inspired algorithm for unsupervised classification is proposed. The new
algorithm is based on clonal selection principle and named Unsupervised Clonal
Selection Classification (UCSC). The new proposed algorithm is almost parameter
free. The algorithm parameters are data driven and it adjusts itself to make
the classification as fast as possible. The performance of UCSC is evaluated.
The experiments show that the proposed UCSC algorithm has a good performance
and more reliable.
