Abstract
In our earlier papers, we introduced our adaptive
program called "STROGANOFF" (i.e. STructured
Representation On Genetic Algorithms for Non-linear
Function Fitting), which integrated a multiple
regression analysis method and a GA-based search
strategy. The effectiveness of STROGANOFF was
demonstrated by solving several system identification
problems. This paper proposes an ädaptive
recombination" mechanism for STROGANOFF. Our
intention is to exploit already built structures by
ädaptive recombination", in which GP recombination
is guided by a certain measure. The effectiveness of
our approach is shown by the experiment in predicting a
chaotic time series. Thereafter we describe real-world
applications of STROGANOFF to computer vision.
- adaptive
- algorithm-based
- algorithms,
- analysis
- analysis,
- chaotic
- computer
- estimation,
- fitting,
- function
- genetic
- identification
- mechanism,
- method,
- multiple
- nonlinear
- numerical
- prediction,
- problems
- problems,
- program
- program,
- programming,
- recombination
- recombination,
- regression
- representation,
- search
- series
- series,
- statistical
- strategy,
- stroganoff,
- structured
- system
- time
- vision,
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