Research On RNA Genetic Algorithms And Applications

As an effective method to solve complex optimization problems, genetic algorithm (GA) has being attracted people's attention since it proposed. Being global search techniques, GAs simulate the processes of natural evolution and own some remarkable advantages, such as excellent global exploration ability and good applicability. However, traditional genetic algorithms have some drawbacks, such as poor local searching ability and premature convergence etc. Inspired by RNA molecular coding and operations, RNA genetic algorithms and their applications are studied in this dissertation. The main contents are as follows:(1) These individuals in population are becoming more and more similar with evolution, which makes GA be prone to premature convergence. In order to overcome this drawback, the RNA genetic algorithm with similar individuals rejected (srRNA-GA) is proposed. The algorithm adopts encoding of the nucleotide bases and RNA permutation operation and RNA stem-loop operation are designed for the proposed genetic algorithm. In selecting operation, the strategy of rejecting similar individuals is applied to improve population diversity and avoid premature convergence. The performances of the proposed RNA-GA are validated by five benchmark functions. The efficiency and accuracy of this proposed algorithm are demonstrated in the three practical parameter estimation problems.(2) Inspired by the expression of bio-genetic information, a RNA genetic algorithm based on protein characteristics is proposed. In this algorithm, the RNA recoding operation and protein folding operation are designed to replace conversional crossover operation by simulating the process from DNA molecular to protein ones in biology. Numerical experiments with some benchmark functions and the parameter estimation problem in hydrocracking of heavy oil demonstrate the effectiveness of this proposed algorithm.(3) In order to overcome the drawback setting GA's parameter blindly, the RNA genetic algorithm with dynamic mutation probability according to entropy is proposed. In this algorithm, the values of mutation probability are decided by nucleotide bases distribution of the current bits of population. The numerical results on four benchmark functions show the effectiveness of this proposed algorithm. The solution of the short-time gasoline blending scheduling problem shows that the proposed algorithm gain a higher profit.(4) The membrane structure based hierarchical RNA genetic algorithm is proposed. In this algorithm, the RNA genetic algorithms are imbedded into the membrane system and the final results of the RNA-GAs in the two membrane sub-systems are used to determine a part of initial population of the RNA-GA in the skin membrane system. Numerical results on six benchmark functions demonstrate excellent search performance of this proposed algorithm. The algorithm is applied to solve short-time gasoline blending scheduling problem. The experimental results show that this proposed algorithm can obtain a higher profit.(5) The RNA genetic algorithm with a changeable problem search space is presented for constrained nonlinear optimization problems. Along with running, the changeable problem search space is re-built for promoting the search efficiency. The performances of this proposed algorithm are validated by five benchmark functions. It is also used to model the CSTR process by a neural network. The simulation results show that higher precision and lower complexity are reached.