An extended mind evolutionary computation model for optimizations

摘要

The paper makes an analysis on the simulated mechanisms of mind evolutionary computation (MEC) firstly and proposes an extended computation model for MEC (EMEC). EMEC manipulates the search based on the behavior space and the information space. All operations in the behavior space are processed based on groups that symbolize the solution area, while the operations in the information space are done based on the billboards that are used to record the evolutionary information. All components of EMEC are formulated in details, including the similar-taxis operation, the cooperation operation, and a simulated-annealing-based dissimilation operation (SADO). EMEC emphasizes on the share and the guide of the information in the search, and gets a performance superior to the simple MEC. The proposed EMEC was performed on some well-known benchmark problems. The experimental results show EMEC is a robust global optimization algorithm and can alleviate the premature convergence validly.