System identification using structured genetic algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{iba:1997:HECb,

• author = "Hitoshi Iba",
• title = "System identification using structured genetic algorithms",
• booktitle = "Handbook of Evolutionary Computation",
• publisher = "Oxford University Press",
• publisher_2 = "Institute of Physics Publishing",
• year = "1997",
• editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",
• chapter = "section G1.4",
• keywords = "genetic algorithms, genetic programming, stroganoff, gmdh, sgpc version 1.1",
• ISBN = "0-7503-0392-1",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
• broken = "doi:10.1201/9781420050387.ptg",
• URL = "https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",
• size = "11 pages",
• abstract = "This case study describes a new approach to system identification problems based on genetic programming (GP), and presents an adaptive system called STROGANOFF (structured representation on genetic algorithms for nonlinear function fitting). STROGANOFF integrates an adaptive search and a statistical method called group method of data handling (GMDH). More precisely, STROGANOFF consists of two processes: (i) the evolution of structured representations using a traditional genetic algorithm and (ii) the fitting of parameters of the nodes with a multiple-regression analysis. The fitness evaluation is based on a minimum-description-length (MDL) criterion. Our approach builds a bridge from traditional GP to a more powerful search strategy. In other words, we introduce a new approach to GP, by supplementing it with a local hill climbing. The approach is successfully applied to a time-series prediction.",

}

Genetic Programming entries for Hitoshi Iba

Citations