Research On Sorting Unsigned Permutation By Reversals Based On Parallel Immune Genetic Algorithm

The genetic material of organisms changes as they evolve.As opposed to point change at a gene or several genes, More and more researchers were concerned with large transformations at the genome level.Genome rearrangement is an important mode of biology molecular evolution and also an important problem of computational molecular biology research.Especially The mathematics character and algorithm research based on genome rearrangement by reversals are all attention.Thereout,The problem of sorting unsigned permutation by reversals is inspired by genome rearrangement.Given two genomes respresented as two permutations, to find a most parsimonious scenario of sorting by reversals that transforms one permutation into the other. Genetic algorithm is a probabilistic search algorithm based on nature selection and biology evolution mechanism.it develops and researchs for more than thirty years. Immunology has already become a subject; more and more researchers are paying attention to it. Parallel computational is an energy field, it also develops for several years. The production of parallel computational can be found everywhere in science and technique.In this paper, an immune genetic algorithm for the problem of genome rearrangements of the sorting unsigned permutation by reversals was proposed, it added an immune operator to traditional genetic algorithm and promoted the viability of some individuals in population by vaccination.The immune genetic algorithm is a new integrative algorithm which contains immune mechanism and evolution mechanism. It is a new attempt to use this algorithm to sort unsigned permutation by reversals. The precocial characteristic of genetic algorithm can be avoid, at the same time, parallel computational can improve astringency.Because the problem of genome rearrangements of the sorting unsigned permutation by reversals has been proved to be a NP-hard problem. The existing algorithms are approximate algorithms; it can be improve the precision of the approximation.