【作者】 侯建花；

【导师】 罗省贤；

【作者基本信息】 成都理工大学 ， 应用数学， 2004， 硕士

【摘要】 遗传算法是一种模拟自然界生物进化的搜索算法，由于它简单易行、鲁棒性强，尤其是不需要专门的领域知识而仅用适应度函数作评价来指导搜索过程，从而使它的应用范围极为广泛，并且已在众多领域得到了实际应用，取得了令人瞩目的成果，引起了广大学者和工程人员的关注。 遗传算法是一种新兴的技术，正处于发展期。虽然在应用领域获得了丰收，但其理论基础还较薄弱，有许多地方需要研究和发展充实。 本文对遗传算法的理论与应用进行了一些研究和分析工作。首先介绍了遗传算法的理论和它在组合优化问题中的应用，然后针对基于遗传算法的TSP问题求解，在原有遗传算法的基础上提出了一种改进的混合遗传算法。该算法在迭代初期引入不适应度函数作为评价标准，结合启发式交叉和边重组交叉算子设计了一种新的交叉算子，采用了模式变异和启发式变异相结合的混合变异算子，并对变异后个体进行免疫操作。数值实验表明，该算法是有效的。最后，为克服遗传算法计算量大的问题，基于遗传算法的并行特性，实现了一种主从式并行混合遗传算法，实验数值结果证明了该算法的可行性和有效性。更多还原

【Abstract】 The genetic algorithm is a kind of searching method which simulates the natural evolution. It is simple and easy to implement, especially it doesn’t need the special field knowledge, so it has been used in every broad fields. Now the genetic algorithm has got a lot of fruits and more scholars begin to pay attention to it.The genetic algorithm is still a new developing technology. Despite its success in so many domains, its theoretical fundament is relatively weak. There are still lots of problems to be studied and improved.This paper has done some work in the research and application of the genetic algorithm. First, The introduction on GA’s theory and its applications on combination optimization are given. An improved hybrid genetic algorithm is then presented for TSP problem. In this algorithm, the unfitness function is chosen as a merit at the beginning of iterative and a new cross operation is designed.In addition, we use a hybrid mutation operation, and make immune operation on individual after mutation operation. The simulation numerical results show that this algorithm is efficient. At last, to overcome GA’s large computation consumption, a master/slave parallel hybrid genetic algorithm is implemented based on the GA’s parallel characteristic. The experiment results demonstrate that the method is valid and efficitive.更多还原