基于基因表达式的演化硬件进化和优化算法

电路进化设计是可进化硬件研究的重要内容．针对电路进化设计做了如下工作：(1)融合了数据挖掘、基因表达式编程与传统电路进化技术，提出两阶段电路进化方法．该方法包括基于表达式树遗传编程进化算法的电路进化阶段和基于挖掘频繁数字电路算法的电路优化阶段。(2)给出了详尽的实验．实验表明6次多项式函数发现的平均进化代数为442代、乘法器电路的平均进化代数为2292代．比笛卡尔遗传编程和NEHF(Novel Evolvable Hardware Framework)快6倍以上．用MFDC对乘法器电路进化结果进行挖掘后，得到了比传统电路更有效的乘法器电路。

Evolution and Optimization for Evolvable Hardware with Gene Expression Programming

Evolutionary design of electronic circuits is an important aspect in the research of Evolvable HardWare (EHW).The main contribution of this paper includes: (1) Based on fusing the techniques in data mining, gene expression programming and traditional EHW, this paper proposes a two-phased EHW framework including evolution phase based on ETGP (Express Tree Genetic Programming) algorithm and optimization phase based on MFDC (Mining Frequency Digital Circuit) algorithm. (2) By extensive experiments show that the average number of generation needed for the sixth order polynomial regression is 442, and the number of generations for multiplier is 2292. The figure is 6 times faster than CGP (Certain Genetic Programming) and NEHF (Novel Evolvable Hardware Framework). As a result, it gives the optimization circuit of multiplier by MFDC that is more efficient than the traditional circuit.