[[PageOutline]]
= Additional Material for Publications = 
This page contains a collection of additional material related to publications of members of the research group HEAL. 

== 2010 ==

=== 22nd European Modeling & Simulation Symposium (EMSS) ===

==== Mutation Effects in Genetic Algorithms with Offspring Selection Applied to Combinatorial Optimization Problems ====

Authors: S. Wagner, M. Affenzeller, A. Beham, G. Kronberger, S.M. Winkler

The HeuristicLab experiments described in the paper can be downloaded [source:misc/publications/2010/conferences/EMSS/swagner here].

=== Dissertation Kronberger ===
The following datasets are used in experiments in the thesis.
==== Artificial benchmark datasets ====
===== Friedman-I =====
[export:/misc/publications/2010/theses/gkronber/friedman-I.csv friedman-I.csv]

This dataset is described in (Friedman, 1991), where it is used to benchmark the multi-variate adaptive regression splines (MARS) algorithm. The signal-to-noise ratio in this dataset is rather low, so it is difficult to rediscover the generating function f(x) especially the terms below the noise level (x4 and x5).

[[Image(friedman-I.png)]]

Variables x01,..., x10 are sampled uniformly from the unit hypercube (x~U(0,1)).
Epsilon is generated from the standard normal distribution (e~N(0,1)).

===== Friedman-II =====
     [export:/misc/publications/2010/theses/gkronber/friedman-II.csv friedman-II.csv]

This dataset is also described in (Friedman, 1991). The signal-to-noise ratio in this dataset is larger compared to the Friedman-I function.

[[Image(friedman-ii.png)]]

Variables x1,..., x5 are sampled uniformly from the unit hypercube (x~U(0,1)).

===== Breiman-I =====
     [export:/misc/publications/2010/theses/gkronber/breiman-I.csv breiman-I.csv]
This dataset is described in (Breiman et al., 1984), where it is used to benchmark the classification and regression trees (CART) algorithm. The signal-to-noise ratio is rather low and additionally it contains a crisp conditional which makes it rather difficult to rediscover the generating function with a symbolic regression approach.

[[Image(breiman-I.png)]]

Epsilon is generated from the normal distribution (e~N(0,2)).

Variables x01,..., x10 are randomly sampled attributes following the probability distributions:

[[Image(breiman-I-variables.png)]]

==== Real-world datasets ====

===== Chemical-I =====
     [export:/misc/publications/2010/theses/gkronber/chemical-I.csv chemical-I.csv]

===== Chemical-II =====
     [export:/misc/publications/2010/theses/gkronber/chemical-II.csv chemical-II.csv]

===== Financial-I =====
     [export:/misc/publications/2010/theses/gkronber/financial-I.csv financial-I.csv]

===== Macro-Economic =====
     [export:/misc/publications/2010/theses/gkronber/macro-economic.csv macro-economic.csv]

===== Housing =====
     [export:/misc/publications/2010/theses/gkronber/housing.csv housing.csv]

==== References ====
Jerome H. Friedman, Multivariate adaptive regression splines, ''The Annals of Statistics'', 19(1):1-141, 1991. \\
Leo Breiman, Jerome H. Friedman, Charles J. Stone and R. A. Olson, ''Classification and Regression Trees'', Chapman and Hall, 1984\\

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== 2011 ==

=== 13th International Conference on Computer Aided Systems Theory (eurocast) ===



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