Computational Issues in Micro-Genetic Algorithms for Traffic Management
Computational Issues in Micro-Genetic Algorithms for Traffic Management
复制标题
交通管理微遗传算法的计算问题
DOI：
10.3141/1679-15
复制
发表时间：
1999
影响因子：
1.7
通讯作者：
R. Benekohal
中科院分区：
文献类型：
--
作者：
G. Abu;R. Benekohal
新兴的人工智能技术，如遗传算法（GAs）允许一个更现实的表示和解决困难和组合的问题，如动态交通队列管理问题。计算经验，在解决这样复杂的大规模问题，通过使用微型遗传算法。除了提供证据的能力，微GA成功地确定最佳的交通管理方案，一些微GA相关的计算问题，值得注意的突出。选择一个合适的群体大小是一个关键的决定，内部变异性必须考虑到适当的优化结果的好坏。提出了一个简单的规则来决定最佳的人口规模的微型遗传算法。微遗传算法可能收敛到低质量的解决方案，特别是在人口规模非常小的情况下;微遗传算法本身的收敛并不足以表明良好的性能。一些真实世界系统的搜索空间的大小可能会给微型遗传算法带来一些困难。在微遗传算法和常规遗传算法之间进行选择是一个依赖于问题的决定。
Emerging artificial intelligence techniques such as genetic algorithms (GAs) allow a more realistic representation and solution of difficult and combinatorial problems such as the dynamic traffic queue management problem. Computational experience in solving such complex large-scale problems by use of micro-GAs is described. In addition to providing evidence of the ability of micro-GAs to successfully identify optimal traffic management schemes, some micro-GA-associated computational issues that warrant attention are highlighted. Choosing a proper population size is a critical decision, and internal variability must be accounted for to assess the goodness of the optimization results properly. A simple rule for deciding the best population size for micro-GAs is proposed. Micro-GAs may converge to low-quality solutions, particularly with very small population sizes; convergence of micro-GAs by itself is not a sufficient indication of good performance. The size of the search space for some real-world systems can pose some difficulties to micro-GAs. Choosing between micro-GAs and regular GAs is a problem-dependent decision.