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Studies on Immune Clonal Selection Algorithm and Application of Bioinformatics
Authors: Hongbing Zhu, Jun Wu, Jinguang Gu

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Immune algorithms (IAs) are microscopic view of evolutionary algorithms (EAs) and applied in combinatorial
optimization problems. This paper addresses to a clonal selection algorithm (CSA) that is one of the most
representative IA and was applied into the protein structure prediction (PSP) on AB off-lattice model, in which the
memory B cells of the CSA was innovated by employing different strategies: local search and global search in the
phase of the mutation. And the CSA was further improved by adding aging operator to combat the premature convergence.
However the pure aging operator didn’t achieve effective results and sometimes the optimum solution was
eliminated. To resolve this problem, the current best solution was reserved by an antibody and it was not eliminated
when its age reached its life span. In our experiments the improved algorithm was compared with the standard CSA
and the pure aging CSA, which of the results demonstrated that the improved strategy with the memory B cells and
long life aging was very effective to overcome premature convergence and to avoid trapped in the local-best solution,
and it was also effective in keeping the diversity of the small size population. On the other hand, one novel hybrid
algorithm Quantum Immune(QI), which combines Quantum Algorithm (QA) and Immune Clonal Selection(ICS) Algorithm,
has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice
model in the predicting 2D protein folding structure. Clonal Selection Algorithm was introduced into the hypermutation
operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was
designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into
the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of
the algorithm. Experimental results showed that the lowest energies and computing-time of the improved Quantum
Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by
adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS
algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization
algorithm.