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Additional Material for Publications
This page contains a collection of additional material related to publications of members of the research group HEAL.
In process
Annals of Operations Research
The publication "Distribution of Waiting Time for Dynamic Pickup and Delivery Problems" to this journal is currently in review.
The following additional material is provided:
Test Environment (Executable including Sample)
Source code of the waiting strategies
IEEE Transactions on Industrial Informatics
A publication to this journal is currently accepted, but still in review.
The described experiments shall be made available as follows. For running the simulation model, MatPower is needed with Matlab (link below). The HeuristicLab file contains the optimization runs as well as results that have been analyzed within this paper.
Simulation Model & Optimization Experiments
Simulation File for Matlab Simulation Model
Matpower Data File 118-Bus Data File
HeuristicLab 3.3 Experiment Files HeuristicLab Experiments Collection
Simulation-based optimization with HeuristicLab
A description of how to provide an evaluation service for AnyLogic simulation models is given here?.
Links
MATPOWER - A MATLAB Power System Simulation Package, retrieved 11.1.2013
Protocol buffers, retrieved 11.1.2013
Matlab Automation Server, retrieved 11.1.2013
2014
PPSN 2014
13th International Conference on Parallel Problem Solving from Nature, 13th to 17th September, 2014, Ljubljana, Slovenia
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo experiment and results: exp.hl exp_results.hl
2013
GPTP 2013
Genetic Programming Theory and Practice Workshop, 9th to 11th May, 2013, Ann Arbor, USA
Gaining Deeper Insights in Symbolic Regression: Theoretical and Practical Issues (Keynote)
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo experiment and results: exp.hl exp_results.hl
GECCO 2013
Genetic and Evolutionary Computation Conference, July 6th to 10th, 2013, Amsterdam, Netherlands
Effects of Constant Optimization by Nonlinear Least Squares Minimization in Symbolic Regression
Experiments and Results:
Friedman-2.hl
Keijzer-6.hl
Nguyen-7.hl
Pagie-1.hl
Poly-10.hl
Tower.hl
Vladislavleva-4.hl
2012
ECML-PKDD 2012
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), September 24th to 28th, 2012, Bristol, UK
HeuristicLab Demo: Knowledge Discovery through Symbolic Regression with HeuristicLab
All material is available at the dedicated demo page http://dev.heuristiclab.com/AdditionalMaterial/ECML-PKDD
GECCO 2012
Genetic and Evolutionary Computation Conference, 7th to 11th July, 2012, Philadelphia, USA
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo TSP instance from TSPLIB: ch130.tsp ch130.opt.tour
Demo experiment and results: exp.hl exp_results.hl
Demo dataset for symbolic regression: poly-10.csv
Demo dataset for symbolic classification: mammography.csv
APCast 2012
14th International Asia Pacific Conference on Computer Aided System Theory, 6th to 8th February, 2012, Sydney, Australia
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo TSP instance from TSPLIB: ch130.tsp ch130.opt.tour
Demo experiment and results: exp.hl exp_results.hl
2011
13th International Conference on Computer Aided Systems Theory (eurocast)
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo dataset for symbolic regression: polynomial.csv
Demo dataset for symbolic time series modeling: Mackey-Glass-17.txt
GECCO 2011
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo TSP instance from TSPLIB: ch130.tsp ch130.opt.tour
Demo experiment and results: exp.hl exp_results.hl
Demo dataset for symbolic regression: poly-10.csv
Demo dataset for symbolic classification: mammography.csv
evo* 2011
Macro-economic Time Series Modeling and Interaction Networks
Data set of macro economic variables: macroeconomicdata.txt
ICCGI 2011
6th International Multi-Conference on Computing in the Global Information Technology, 19th of June, 2011, Luxemburg
Tutorial Algorithm and Experiment Design with HeuristicLab
Demo dataset for symbolic regression: poly-10.csv
Demo dataset for symbolic classification: mammography.csv
LINDI 2011
3rd IEEE International Symposium on Logistics and Industrial Informatics, August 25-27, 2011 in Budapest, Hungary
Demo dataset for warehouse slotting
Stock keeping units: SKUsLindi.txt
Order profile: OrderProfileLindi.txt
IMMM 2011
1st International Conference on Advances in Information Mining and Management, 23th of October, 2011, Barcelona
System Identification and Data Mining with HeuristicLab
Demo TSP instance from TSPLIB: ch130.tsp ch130.opt.tour
Demo experiment and results: exp.hl exp_results.hl
Demo dataset for symbolic regression: poly-10.csv
Demo dataset for symbolic classification: mammography.csv
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 here.
Dissertation Kronberger
The following datasets are used in experiments in the thesis.
Artificial benchmark datasets
Friedman-I
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).
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
This dataset is also described in (Friedman, 1991). The signal-to-noise ratio in this dataset is larger compared to the Friedman-I function.
Variables x1,..., x5 are sampled uniformly from the unit hypercube (x~U(0,1)).
Breiman-I
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.
Epsilon is generated from the normal distribution (e~N(0,2)).
Variables x01,..., x10 are randomly sampled attributes following the probability distributions:
Real-world datasets
Chemical-I
Chemical-II
Financial-I
Macro-Economic
Housing
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
Attachments (4)
- friedman-I.png (5.2 KB) - added by gkronber 14 years ago.
- breiman-I.png (7.0 KB) - added by gkronber 14 years ago.
- breiman-I-variables.png (6.9 KB) - added by gkronber 14 years ago.
- friedman-ii.png (8.1 KB) - added by gkronber 14 years ago.
Download all attachments as: .zip
