模式定理 meaning in Chinese
scheme theorem
Examples
- The modified genetic algorithm that is analyzed with the schema theorem is feasible
以模式定理论述改进遗传算法的可行性。 - The paper discusses the basic theory of genetic algorithms including schemate theorem , building block hypothesis , implicit parallelism , the analysis of astringency and so on , as the theoretical base of application
在对遗传算法的阐述中，讨论了遗传算法的基本原理，包括模式定理、积木块假设、隐含并行性和遗传算法的收敛性分析等，作为后面遗传算法应用的理论依据。 - ( 2 ) considering the problem that genetic algorithm running result is affected greatly by the initial parameter and it ’ s easy to trap in local optimum , based on its mathematic theory , studied deeply on self - adaptive genetic algorithm , presented a new viewpoint - - superiority inheritance , furthermore , designed adaptive genetic algorithm based on superiority inheritance . the experiment showed that this algorithm could find the global best solution , and had strongly global hunting capability . it is prior to traditional adaptive genetic algorithms on aspects of accuracy , stability , and repeatability etc . it was applied in fast image
( 2 )针对遗传算法的初始参数对算法结果影响较大和易陷入局部最优的问题,在阐述基本数学理论模式定理的基础上,对自适应遗传算法进行了深入的研究,提出了一种优势遗传的新观点,由此设计了基于优势遗传的自适应遗传算法,通过实验表明,该算法能够达到理想全局最优解,有很强的全局搜索能力,在准确性、稳定性和重复性方面优于当前自适应遗传算法。 - Finally , genetic optimization research is summarized on several typical production scheduling problems . after expounding the general idea of genetic algorithm , the comparative advantages in contrast to the traditional algorithm , the basic characteristics of genetic algorithm and its theoretical base , the paper puts emphasis on the efficiency of genetic algorithm in the scheduling of flow shop , and puts forward an improving genetic algorithm : the ordinal genetic algorithm based on the heuristic rules . the new algorithm introduces into the initial group the solution of heuristic algorithm , and in the group structure adopts a strategy of first ordering according to the priority of the adaptive solution , and then defining a new way of choosing probability by segments , which provides more hybridizing opportunity for optimized individuals , and designs variation - control rule to prevent single population and partial optimal solution
在论述了遗传算法的思想、与传统搜索算法的比较优势、遗传算法的基本特征和遗传算法的理论基础(包括模式定理、隐含并行性、基因块假设、欺骗问题和收敛性定理)后，重点探讨了遗传算法在flowshop调度问题中的潜力和有效性；结合启发式规则，提出了一个改进的遗传算法?基于启发式规则的有序遗传算法，新算法在初始种群中引入了启发式算法的解，在种群结构上采用了先按适应值优劣排序再分段确定选择概率的新策略，使优质个体有更多的杂交机会，在变异中设计了变异控制规则，以防种群单一化，而陷入局部优化解。