Genetic programming sustainable evolutionary algorithm based on adaptive migration mechanisms

This paper improved the search manner of HFC (Hierarchical Fair Competition) model, and introduced an optimization method which was mixed with structure parameters based on genetic programming (GP). Meanwhile, some adaptive mechanisms were added to HFC model, and then generated three derived HFC models: static generational HFC model (SHFC) which added an opened topology search to the initial HFC model; the generational HFC model with admission threshold adaptation mechanism (HFC-ADM) which set the admission thresholds of all ranks adaptively, and the generational HFC model with adaptive migration topology (HFC-ATP) which permits the individuals moved to different ranks dynamically. At last, we take the traveling salesman problem as benchmark; use the three models SHFC, HFC-ADM and HFC-ATP based on GP to solute this famous problem. We also use GA in this benchmark problem as a comparison. The result indicated these three improved HFC models based on GP achieved sustainable evolution, and reduced the randomicity of algorithms efficiently.