基于多目标基因表达式编程的电路演化算法

为提高电路演化的效率和成功率，对电路设计中涉及的多个目标进行了定义与量化，并针对多目标优化问题，在基因表达式编程（GEP：Gene Expression Programming）的基础上，提出了基于多目标基因表达式编程的电路演化算法（MGEP：Multi-Objective Gene Expression Programming）。设计了演化电路中的GEP编码，定义和量化了电路演化的多个目标，利用非支配排序和适应度共享策略提高搜索方向的空间均匀性。通过数字电路演化实验证明，MGEP算法与GP算法相比进化时间减少了72．9％，同时得到的电路更简单实用，得到最优电路的比率分别比GP和传统的GEP提高了50．4％和38．9％。

Evolutionary Algorithm of Circuit Based on Multi-Objective Gene Expression Programming

Evolution of circuit is a focus of EHW （Evolvable Hardware）. To improve the evolution efficiency and success rate of circuits, the muhi-objectives during evolution of circuit are defined and quantized. To solve the multi-objective optimization, an evolutionary algorithm of circuits based on MGEP （Multi-Objective Gene Expression Programming） is presented. The chromosome encoding of GEP （Gene Expression Programming） in evolution of circuit is designed; the multi-objectives during evolution of circuit are defined and quantized. And the uniformly scattered search direction is enhanced by non-dominated sorting and fitness sharing strategy. The experiments on evolution of digital circuits show that MGEP improves the evolutionary efficiency. Compared with GP, the evolutionary time of MGEP drops 72. 9%. MGEP is also capable of searching out simple and practical circuit. Compared with GP and GEP, the ratio of searching optimal circuit increases 50. 4% and 38.9%.