摘要
遗传算法是一种约束随机优化方法。它与传统的确定性优化方法相比较，具有全局收敛，不计算目标函数偏导数等优点；与传统的随机优化方法相比较，具有搜索效率高以及隐含并行计算等优点。本文首先讨论了遗传算法的基本原理和迭加机算步骤，其次讨论了该方法在磁化强度约束反演问题中的应用效果。
Genetic algorithm is a constrained stochastic optimization method. Compared with traditional deterministic optimization method, it has the merits of realizing overall convergence with no need of calculating partial derivatives of objective functions; compared with traditional stochastic optimization method, it has the advantages of high search efficiency and implication parallel calculation. The present paper deals with the basic principles of genetic algorithm and procedures of iterative calculation, followed by a discussion on the application results of this method in the constrained inverse problem of magnetic intensity.
出处
《物探与化探》
CAS
CSCD
1996年第3期202-208,共7页
Geophysical and Geochemical Exploration
关键词
遗传算法
磁异常
约束反演
应用效果
磁法勘探
genetic algorithm
magnetic anomaly
constrained inversion
application result
