A researcher can infer mathematical expressions of
functions quickly by using his professional knowledge (called
Prior Knowledge). But the results he finds may be biased and
restricted to his research field due to limitation of his knowledge.
In contrast, Genetic Programming method can discover fitted
mathematical expressions from the huge search space through
running evolutionary algorithms. And its results can be generalized
to accommodate different fields of knowledge. However,
since GP has to search a huge space, its speed of finding the
results is rather slow. Therefore, in this paper, a framework
of connection between Prior Formula Knowledge and GP (PFK-GP)
is proposed to reduce the space of GP searching. The PFK
is built based on the Deep Belief Network (DBN) which can
identify candidate formulas that are consistent with the features
of experimental data. By using these candidate formulas as the
seed of a randomly generated population, PFK-GP finds the right
formulas quickly by exploring the search space of data features.
We have compared PFK-GP with Pareto GP on regression of
eight benchmark problems. The experimental results confirm
that the PFK-GP can reduce the search space and obtain the
significant improvement in the quality of SR.
Abstract-In order to solve the problem of modeling and rendering of large data scene, this paper presents a fast method for modeling and rendering multi-level, multi-resolution road based on the road information extraction of simple attribute remote sensing image. Firstly, Fuzzy C-means Clustering (FCM), Bwareaopen, Mathematical Morphology and Markov Random Field (MRF) road extraction methods are used to extract the road skeleton information and road center control points of satellite remote sensing images. Then, the cubic B-spline curve function is used to fit the control information of the road center into the central line of the road. Finally, according to the terrain resolution levels, we will rebuilt the road model, which is rendered at multiple levels through the terrain resolution level by modifying the terrain data to achieve the effect of long-distance fuzzy and close-distance clearness. Experiments show that this method can realize the modeling and rendering of large data scenes effectively and vividly.
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