EGIPSYS: AN ENHANCED GENE EXPRESSION PROGRAMMING APPROACH FOR SYMBOLIC REGRESSION PROBLEMS †

This paper reports a system based on the recently proposed evolutionary paradigm of gene expression programming (GEP). This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed improvements were tested separately, and proved to be advantageous over the basic GEP. EGIPSYS was also applied to four difficult identification problems and its performance was compared with a traditional implementation of genetic programming (LilGP). Overall, EGIPSYS was able to obtain consistently better results than the system using genetic programming, finding less complex solutions with less computational effort. The success obtained suggests the adaptation and extension of the system to other classes of problems.

[1]  Nguyen Xuan Hoai,et al.  Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  M. Moonen,et al.  On- and off-line identification of linear state-space models , 1989 .

[3]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[4]  Li Feng,et al.  A new genetic programming approach in symbolic regression , 2003 .

[5]  Hugh Glaser,et al.  Parallel Implementation of a Genetic-Programming Based Tool for Symbolic Regression , 1998, Inf. Process. Lett..

[6]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[7]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[8]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[9]  D. Rumelhart,et al.  Predicting sunspots and exchange rates with connectionist networks , 1991 .

[10]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[11]  Cândida Ferreira,et al.  Function Finding and the Creation of Numerical Constants in Gene Expression Programming , 2003 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  T. J. McAvoy,et al.  Dynamics of pH in Controlled Stirred Tank Reactor , 1972 .

[14]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.