Recombination guidance for numerical genetic programming
Hitoshi Iba, Taisuke Sato, Hugo deGaris · 2002
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 “adaptive recombination” mechanism for STROGANOFF. Our intention is to exploit already built structures by “adaptive 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.