- author = "I. Yoshihara and T. Aoyama and M. Yasunaga",
- title = "GP-Based Modeling Method for Time Series Prediction with Parameter Optimization and Node Alternation",
- booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00",
- year = "2000",
- pages = "1475--1481",
- volume = "2",
- address = "La Jolla Marriott Hotel La Jolla, California, USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, time series, GP based model building, GP based modelling method, backpropagation-like algorithm, complicated functions, fast method, functional forms, model parameters, mutation, node alternation, parameter optimisation, seismic ground motion, time series prediction, backpropagation, parameter estimation, statistical analysis",
- ISBN = "0-7803-6375-2",
-
DOI = "
doi:10.1109/CEC.2000.870828",
- abstract = "A fast method of GP based model building for time series prediction is proposed. The method involves two newly-devised techniques. One is regarding determination of model parameters: only functional forms are inherited from their parents with genetic programming, but model parameters are not inherited. They are optimised by a backpropagation-like algorithm when a child (model) is newborn. The other is regarding mutation: nodes which require a different number of edges, can be transformed into different types of nodes through mutation. This operation is effective at accelerating complicated functions e.g. seismic ground motion. The method has been applied to a typical benchmark of time series and many real world problems",
-
notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 00TH8512,
Library of Congress Number = 00-018644",
