System identification approach to genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Iba:1994:siGP,

• author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
• title = "System identification approach to genetic programming",
• booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence",
• year = "1994",
• pages = "401--406",
• volume = "1",
• address = "Orlando, Florida, USA",
• month = "27-29 " # jun,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, Boolean concept formation, STROGANOFF, adaptive program, adaptive search, local parameter tuning mechanism, minimum description length-based selection criterion, multiple node types, multiple regression analysis, nonlinear function fitting, nonnumerical reasoning, numerical problems, statistical search, structured representation, symbolic reasoning, symbolic regression problems, system identification, tree pruning, tree structures, Boolean functions, identification, search problems, statistical analysis, symbol manipulation, trees (mathematics), tuning",
• size = "6 pages",
• DOI = "doi:10.1109/ICEC.1994.349917",
• abstract = "Introduces a new approach to genetic programming (GP), based on a system identification technique, which integrates a GP-based adaptive search of tree structures and a local parameter tuning mechanism employing a statistical search. In Proc. 5th Int. Joint Conf. on Genetic Algorithms (1993), we introduced our adaptive program called STROGANOFF (STructured Representation On Genetic Algorithms for NOnlinear 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 (numerical) problems. This paper extends STROGANOFF to symbolic (non-numerical) reasoning, by introducing multiple types of nodes, using a modified minimum description length (MDL) based selection criterion, and a pruning of the resultant trees. The effectiveness of this system-identification approach to GP is demonstrated by successful application to Boolean concept formation and to symbolic regression problems",

}

Genetic Programming entries for Hitoshi Iba Taisuke Sato Hugo de Garis

Citations