Gene Expression Programming is effective for function mining. In gene expression usually exist some un-expressed introns. To improve the expression efficiency, this paper makes following contributions:Proposed an evolutionary algorithm embedded gene expression programming (EGEP) based on a new decoding method of gene; Proposed some new concepts, i.e. the maximum expression tree, nested expression tree and spliced expression tree; Analyzed the expression space of gene and the complexity of algorithm. Extensive experiments show that the success rate is improved greatly and under the small size population, the ability of mining function surpasses GEP apparently. In single gene algorithms, when the objective functions are bivariate function and single-variable function, the ratios of the convergence generation of EGEP to that of GEP are 25.5％ and 16.3％respectively; compared with GEP, the success rate of EGEP is averagly increased by 43％ in bivariate function mining.%为了提高表达效率,提出了新的基因解码方案,形成了内嵌基因表达式编程算法EGEP;提出了极大表达树、嵌套表达树和拼接表达树等概念;分析了基因的表达空间和算法的复杂度.实验表明,该算法提高了函数发现的成功率;在小规模种群的函数中其能力明显优于GEP.在单基因情况下,目标为一元函数和二元函数时,EGEP平均成功辈数分别为GEP算法的25.5%和16.3%;各种规模下,在EGEP算法中二元函数的成功率平均比GEP提高43%以上.
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