Inversion algorithms numerically evaluate the mismatch between model and data to guide the search for minima in parameter spaces. In an alternative approach, the numerical evaluation of data misfit can be replaced by subjectively judging the solution’s quality. This widens the class of problems that can be treated within the framework of formal inverse theory—in particular, various geophysical/geological/geodynamic applications in which structural similarity between model and data determines the quality of the fit. In this situation, prior knowledge, experience, and even personal intuition are crucial. This approach also provides a simple way to include such expertise in more traditional numeric applications, e.g., to treat ambiguous problems and disregard geologically unfeasible solutions from the inverse search.
[1]
David E. Goldberg,et al.
Genetic Algorithms in Search Optimization and Machine Learning
,
1988
.
[2]
Alden H. Wright,et al.
Genetic Algorithms for Real Parameter Optimization
,
1990,
FOGA.
[3]
Nostrand Reinhold,et al.
the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.
,
1991
.
[4]
Mrinal K. Sen,et al.
Global Optimization Methods in Geophysical Inversion
,
1995
.
[5]
F. Boschetti,et al.
Inversion of seismic refraction data using genetic algorithms
,
1996
.
[6]
Kansei,et al.
Interactive Evolutionary Computation : Cooperation of computational intelligence and human
,
2022
.