In this paper, built mathematical model on Flow Shop scheduling, put forward a RNA genetic algorithm based on DNA computing to solve the Flow Shop scheduling problems. Adopt RNA four digit system encoding method based on DNA computing and RNA computing operator in genetic algorithm. It resolved the encoding scheme and convergence problem which exists in the conventional genetic algorithm. Under some constraint conditions, this genetic algorithm got simulated. Simulation results showed that this algorithm has a better optimum searching and seeking abilities, made the scheduling results comparatively reasonable and expanded the application of DNA computing.

Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.

With the development of computer and network technology, traditional way of examinations because of too many artificial proportion, has become increasingly unsuited to the modern teaching demands. In this paper, used the popular B/S structure, combined with the MVC pattern in Java EE platform, used Apache Tomcat server, MySQL database technology, designed a suitable test database, utilized improved genetic algorithm to achieve automatic test paper, combined JavaBean and Session technical etc. to complete the automatic scoring and evaluation functions. Used the database connection pool to establish efficient connections, utilized regular expressions for user authentication, used Ajax technology to achieve partial-page refresh functions, the design fully reflected the superiority of the Java EE platform.

In this paper, According to the collaborative manufacturing resources optimization deployment problems, designed subsection crossover and subsection mutation based on process code, adopted fitness scaling method and ranking method to select operators, proposed an improved genetic algorithm based on DNA computation for solving the resources optimization deployment problems, so that the offspring are better able to inherit the good features of parent. Through simulation, tested the designed algorithm performance; by comparing with conventional genetic algorithm test results, it proved the validity of the designed algorithm.

Membrane computing (P system) is a new computing model; it comes from the research of the basic function and structure of creatures cell membrane. It has complex structure and multi-level. It converges quickly and has high quality of the optimal results. In this article, discussed the basic theory of the membrane computing and the steps of the algorithm. Apply Membrane computing principle to the differential evolution algorithm, constructed a hybrid differential evolution algorithm on the basis of the membrane structure. Finally, utilized the Objective Functions to test the new algorithm performance, compared with related algorithms to analyze the advantages and disadvantages of the new algorithm.