A RNA genetic algorithm with entropy based dynamic mutation probability
信息熵动态变异概率RNA遗传算法

The constrained optimization problem becomes a focus and difficulty in science and engineering filed. Inspired by the expression of bio-genetic information of RNA molecular and entropy concept, a RNA genetic algorithm with entropy based dynamic mutation probability (edmp-RGA) is proposed. The algorithm adopts nucleotide base encoding, and RNA recoding operation and protein folding operation are designed to replace the conventional crossover operation. In the algorithm, the values of mutation probability are decided by nucleotide base distribution of the current bits of population. The numerical experiments on four benchmark functions show the effectiveness of the proposed algorithm. The solution to the short-time gasoline blending scheduling problem shows that the proposed algorithm gains a higher profit.