Abstract
Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, the optimization mechanism of the binary-coding VEGA is analyzed. By the geometrical representation of the virus individual, the virus reverse transcription operations is transformed to be equivalent to the crossover among several host individuals in several different generations. As these host individuals may be close to the best solution of the target problem, VEGA's effectiveness is theoretical deterministic.
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