CAJ | 학술논문

在遗传算法优化过程中，引导搜索的主要依据是适应度函数。通过评估常见的几种适应度函数，兼顾保持种群的多样性和算法的收敛性，由乘幂尺度变换，提出了一种改进的乘幂适应度函数。以三个典型的测试函数为例，在相同遗传操作和参数情况下，分别采用常见的与改进的适应度函数进行优化比较。结果表明，所改进的乘幂适应度函数能明显提高算法的收敛精度、收敛速度和收敛稳定性，对提高遗传算法的整体性能有重要的意义。
재유전산법우화과정중，인도수색적주요의거시괄응도함수。통과평고상견적궤충괄응도함수，겸고보지충군적다양성화산법적수렴성，유승멱척도변환，제출료일충개진적승멱괄응도함수。이삼개전형적측시함수위례，재상동유전조작화삼수정황하，분별채용상견적여개진적괄응도함수진행우화비교。결과표명，소개진적승멱괄응도함수능명현제고산법적수렴정도、수렴속도화수렴은정성，대제고유전산법적정체성능유중요적의의。
It is the main factors for fitness functions to guide the search of the genetic algorithm optimization process. The exponential fitness functions are improved by exponentiation scale transformation. They are used to evaluate several common fitness functions to keep their diversity of population and convergence of the algorithms. The optimal computa-tion is compared for the usual and the improved fitness functions under the same conditions of genetic manipulation and their parameters in using three typical test functions. Numerical results show that it is significant for the new fitness func-tions of a power optimal algorithm to improve the overall performance including the accuracy, convergence speed, and convergence stability of the ameliorated genetic algorithms.