TY  - CONF
T1  - Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
T2  - Springer Lecture Notes on Computer Science
Y1  - 2001
A1  - Eggermont, J.
A1  - van Hemert, J. I.
ED  - J. Miller
ED  - Tomassini, M.
ED  - P. L. Lanzi
ED  - C. Ryan
ED  - A. G. B. Tettamanzi
ED  - W. B. Langdon
KW  - data mining
AB  - In this paper we continue our study on adaptive genetic pro-gramming. We use Stepwise Adaptation of Weights to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression prob-lems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 9-783540-418993
ER  - 

TY  - CONF
T1  - Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming
T2  - Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)
Y1  - 2000
A1  - Eggermont, J.
A1  - van Hemert, J. I.
ED  - van den Bosch, A.
ED  - H. Weigand
KW  - data mining
KW  - genetic programming
AB  - In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data clas-sification have indicated that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use SAW to boost per-formance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems.
JF  - Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)
PB  - BNVKI, Dutch and the Belgian AI Association
ER  - 

TY  - CONF
T1  - Adapting the Fitness Function in GP for Data Mining
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - R. Poli
ED  - P. Nordin
ED  - W. B. Langdon
ED  - T. C. Fogarty
KW  - data mining
KW  - genetic programming
AB  - In this paper we describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in EAs for constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining tasks where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-65899-8
ER  - 

TY  - CONF
T1  - Comparing genetic programming variants for data classification
T2  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - E. Postma
ED  - M. Gyssens
KW  - classification
KW  - data mining
KW  - genetic programming
AB  - This article is a combined summary of two papers written by the authors.  Binary data classification problems (with exactly two disjoint classes) form an important application area of machine learning techniques, in particular genetic programming (GP). In this study we compare a number of different variants of GP applied to such problems whereby we investigate the effect of two significant changes in a fixed GP setup in combination with two different evolutionary models
JF  - Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
PB  - BNVKI, Dutch and the Belgian AI Association
ER  - 

TY  - CONF
T1  - A comparison of genetic programming variants for data classification
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - D. J. Hand
ED  - J. N. Kok
ED  - M. R. Berthold
KW  - classification
KW  - data mining
KW  - genetic programming
AB  - In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body.  As a third aspect we look at generational and steady-state models in combination of both features.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-66332-0
ER  -