In the search for a robust and efficient algorithm to be used for computer virus detection, we have developed an artificial immune system genetic algorithm (REALGO) based on the human immune system's use of reverse transcription ribonucleic acid (RNA). The REALGO algorithm provides memory such that during a complex search the algorithm can revert back to and attempt to mutate in a different "direction" in order to escape local minima. In lieu of non-existing virus generic templates, validation is addressed by using an appropriate variety of function optimizations with landscapes believed to be similar to that of virus detection. It is empirically shown that the REALGO algorithm finds "better" solutions than other evolutionary strategies in four out of eight test functions and finds equally "good" solutions in the remaining four optimization problems.
展开▼
