In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions for genetic algorithms, and genetic algorithm toolboxes to solve Markowitz’s mean variance model. The mathematical model for introducing sparse portfolio strategies uses the decomposition method of penalty functions as an algorithm for solving nonconvex sparse optimization strategies to solve financial portfolio problems. The merging speed of the quadratic programming algorithm is fast, and the merging speed depends on the selection of the initial value. The genetic algorithm performs very well in global searches, but local search capabilities are insufficient and the pace of integration into the next stage is slow. To solve this, using a genetic algorithm toolbox is quick and easy. The results of the experiments show that the final solution of the decomposition method of the fine function is consistent with the solution of the integrity of the genetic algorithm. 67% of the total funds will be spent on local car reserves and 33% on wine reserves. When data scales are small, quadratic programming algorithms and genetic algorithms can provide effective portfolio feedback, and the method of breaking down penalty functions to ensure the reliability and effectiveness of algorithm combinations is widely used in sparse financial portfolio issues.

In this paper, a back-drivable and miniature rotary series elastic actuator (RSEA) is proposed for robotic adaptive grasping. A compact arc grooves design has been proposed to effectively reduce the dimension of the RSEA system. The elastic elements could be reliably embedded in the arc grooves without any additional installation structures. The whole RSEA system is characterized as compact, miniature, and modular. The actuating force is controlled via a PI controller by tracking the deformation trajectory of the elastic elements. An underactuated finger mechanism has been adopted to investigate the effectiveness of the RSEA in robotic adaptive grasping. Results reveal that the underactuated finger mechanism could achieve adaptive grasping via the RSEA in a back-drive approach without the requirement of a fingertip force sensor. The RSEA could also exhibit an actuating compliance and a self-sensing characteristic. The actuating compliance characteristic helps in in guaranteeing the safety of human–robot interaction. The RSEA could estimate the external disturbance due to its self-sensing characteristic, which has the potential to replace the fingertip force sensor in grasping force perception applications.