A hybrid genetic algorithm is proposed based on chaos optimization. The optimization process can be divided into two stages every iteration, one is genetic coarse searching and the other is chaos elaborate searching. Genetic algorithm searches the global solutions in the origin space. An elaborate space near the center of superior individuals is divided from the origin space, which is searched by chaos optimization adequately to generate new better superior individuals for genetic operation. The elaborate space can be compressed quickly to accelerate searching rate and enhance the searching efficiency. In this way, the algorithm has global searching ability and fast convergence rate. The simulation results prove that the algorithm can give satisfied results to function optimization problems.

Based on the remove-restore technique, the application of the Tikhonov regularization algorithm to reduce the effect of measurement error in the airborne gravity dada in researched. By the experiments of two kinds airborne gravity data, which having constant system error and casual system error, the compare of different downward continuation algorithm is performed. According to the results of simulations, the Tikhonov regularization algorithm can effectively reduce the effect of height and the measurement error in the airborne gravity data downward continuation compared to other algorithm.

In order to improve the rotation invariant property of the conventional template matching method, a novel template matching based on the polar coordinate was proposed. The origin of the polar coordinate was at the center of the template. And the matching result was central symmetry, which made the method have the translation and rotation invariant properties simultaneously. The recognition process was divided into two phased. In the first phase, gray information was used to complete matching calculation, and some candidate points were selected according to the matching result. In the second phase, edge strong matching of candidate points was completed. The candidate point, where the sum of gray matching value and edge strong matching value was minimal, was determined as the best matching point. Experiments results show that this method can meet the real-time requirement of the TV tracking system.

In order to avoid blind searching before reducing the searching space of optimized variable and enhance searching efficiency in chaos optimization algorithm, a new mutative scale chaos optimization algorithm, Probability Chaos Optimization Algorithm (PCOA) was proposed. The current searching space is searched according to large probability and the origin space is searched according to small probability. Though the searching space is shrunk prematurely, the global optimal point can be found because the origin space is still searched according to small probability, which can overcome the shortcoming of losing the global optimal points owing to prematurely shrinking the searching space of the optimized variables in conventional mutative scale chaos optimization algorithm. The simulation results prove the validity of the algorithm.