DocumentCode :
2323925
Title :
System identification approach to genetic programming
Author :
Iba, Hitoshi ; Sato, Taisuke ; De Garis, Hugo
Author_Institution :
Machine Inference Sect., Electrotech. Lab., Tokyo, Japan
fYear :
1994
fDate :
27-29 Jun 1994
Firstpage :
401
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
Keywords :
Boolean functions; genetic algorithms; identification; search problems; statistical analysis; symbol manipulation; trees (mathematics); tuning; Boolean concept formation; STROGANOFF; adaptive program; adaptive search; genetic algorithms; genetic programming; 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; Genetic algorithms; Genetic mutations; Genetic programming; Humans; Information processing; Laboratories; Neural networks; Regression analysis; System identification; Tree data structures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
Type :
conf
DOI :
10.1109/ICEC.1994.349917
Filename :
349917
Link To Document :
بازگشت