Multi-population Parallel Genetic Algorithm for Economic Statistical Information Mining Based on Gene Expression Programming

Function discovery is an important research direction in data mining and economic statistical target forecast. Gene expression programming (GEP) is a new tool to discovery the function in economic target analysis field. To overcome the deficiency such as pre-maturity and biggish stagnancy generation in GEP, this study (1) Introduces a dynamic mutation operator ( DM-GEP ) and flexibility controlling of population scale (FC-GEP) for more faster jumping local optimum trap and shortening average convergence generation in evolution, (2) Proposes a genome diversity-guided of grading evolution strategy for stakeout and melioration of GEP evolution process, (3) implements a multi-genome child-population parallel genetic strategy and a PED- GEP algorithm for increasing average maximal fitness and success ratio, and (4) demonstrates the effectiveness and efficiency of the new algorithm by extensive experiments, Comparising with transitional GEP, the average convergence generation is decrease to 35 % at least, and average maximal fitness increases 8 %leastways.