%0 Conference Paper %1 iba:1885:rgn %A Iba, Hitoshi %A Sato, Taisuke %A de Garis, Hugo %B 1995 IEEE Conference on Evolutionary Computation %C Perth, Australia %D 1995 %I IEEE Press %K STROGANOFF, 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, structured system time vision, %P 97--102 %T Recombination Guidance for Numerical Genetic Programming %V 1 %X 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.

@inproceedings{iba:1885:rgn, 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 {"}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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Perth, Australia}, author = {Iba, Hitoshi and Sato, Taisuke and {de Garis}, Hugo}, biburl = {https://www.bibsonomy.org/bibtex/2ae2869a79548797d0257650147078950/brazovayeye}, booktitle = {1995 IEEE Conference on Evolutionary Computation}, interhash = {adcbd97e0df8a2388d26b1f91f128bb1}, intrahash = {ae2869a79548797d0257650147078950}, keywords = {STROGANOFF, 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, structured system time vision,}, month = {29 November - 1 December}, notes = {ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Female face outline. Stately home Hursley house windows.}, pages = {97--102}, publisher = {IEEE Press}, publisher_address = {Piscataway, NJ, USA}, size = {6 pages}, timestamp = {2008-06-19T17:42:02.000+0200}, title = {Recombination Guidance for Numerical Genetic Programming}, volume = 1, year = 1995 }