GENETIC ALGORITHMS - AN EVOLUTION FROM MONTE-CARLO METHODS FOR STRONGLY NONLINEAR GEOPHYSICAL OPTIMIZATION PROBLEMS

被引:56
作者
GALLAGHER, K
SAMBRIDGE, M
DRIJKONINGEN, G
机构
[1] UNIV CAMBRIDGE,INST THEORET GEOPHYS,CAMBRIDGE,ENGLAND
[2] DELFT UNIV TECHNOL,DEPT MIN & PETR ENGN,DELFT,NETHERLANDS
[3] UNIV LONDON UNIV COLL,DEPT GEOL SCI,LONDON WC1E 6BT,ENGLAND
关键词
D O I
10.1029/91GL02368
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In providing a method for solving non-linear optimization problems Monte Carlo techniques avoid the need for linearization but, in practice, are often prohibitive because of the large number of models that must be considered. A new class of methods known as Genetic Algorithms have recently been devised in the field of Artificial Intelligence. We outline the basic concept of genetic algorithms and discuss three examples. We show that, in locating an optimal model, the new technique is far superior in performance to Monte Carlo techniques in all cases considered. However, Monte Carlo integration is still regarded as an effective method for the subsequent model appraisal.
引用
收藏
页码:2177 / 2180
页数:4
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