2 results about "Adaptive mutation" patented technology

The application discloses a high-sensitivity parameter determination and optimization method of an enzyme-constrained metabolic network model. First, a sensitivity analysis method is used to sort parameters in the model according to contributions to comparative growth rate prediction results from high to low, and parameters with higher contributions are selected for optimization. Then, a differential evolution algorithm with an adaptive mutation strategy is used for parameter optimization. The algorithm can adaptively select a mutation strategy to improve optimization efficiency. Comparative growth rate data under different growth conditions are obtained from a database, and part of the training set is selected from the data. Finally, a performance evaluation result of the optimized model is obtained through a flux variability and phase plane analysis method. The high-sensitivity parameter is a turnover number kcat parameter of an enzyme.

The application provides a test case adaptive mutation method based on double population crossover learning, comprising: compiling and inserting a program source code of a measured object; obtaining a data packet flow of a measured protocol and loading into a seed queue as a starting point of a fuzzy test process; adopting a test case adaptive mutation algorithm based on double population crossover learning, dividing seeds of each iteration into high-quality seeds and low-quality seeds according to quality degrees, respectively executing different mutation strategies, and expanding a global possible solution search surface; monitoring a system, a memory and a CPU survival state of a program, recording seeds causing abnormality of a target program or system crash, seeds generating new branch paths or seeds generating new state conversions; evaluating seed mutation strategies according to monitoring results, measuring and calculating quality degrees of seeds causing program abnormality, system crash, new branch paths or state conversions, increasing quality degree reward values of the seeds in an iteration process, and obtaining a test global optimal solution.