佳点集遗传算法

该文借助于遗传算法的理想浓度模型以及由此模型对遗传算法的机理的分析，给出了遗传算法的运行机理发及特点，即遗传算法是一个具有定向制导的随机搜索技术，其定向制导的原则蛎：导向以高适应度模式为祖先的“家族”方向，以此结论为基础，利用数论中的佳点休的理论和方法，对GA算法中的交叉操作进行了重新设计，给出了一个新的GA算法，称之为佳点集遗传算法，最后作者将佳点GA算法应用于求解优化问题、SAT问题、TSP问题和背包问题，并与其它求解SAT算法进行比较，通过模拟比较，可以看出新的算法不但提高了算法的速度和精度，而且避免了其它方法常有的早期收敛的现象，这说明作者对GA算法机理的理解和佳点CA算法可能为GA算法的研究开辟一条新的途径。

Good Point Set Based Genetic Algorithm

By analyzing the genetic algorithm(GA) based on its idea density model, the essence and characteristics of GA are given. It is shown that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness. Based on the results, the crossover operation in GA is redesigned by using the principle of good point set in number theory. Then a new GA called good point set based GA is presented. The new GA is applied to optimization problems such as SAT,TSP, etc. Compared to other approaches for solving SAT, the simulation results show that the new GA has superiority in speed, accuracy and overcoming premature. The new interpretation of GA and the proposed good point set based GA provide a new way for investigating GA.