Additional Material for Publications
This page contains a collection of additional material related to publications of members of the research group HEAL.
Page Contents
2021
GPTP 2021
Grammar-based Vectorial Genetic Programming for Symbolic Regression
- Fleck, S. Winkler, M. Kommenda, M. Affenzeller
Benchmark Data Genereation Scripts
2020
GECCO 2020
Operon C++: an Efficient Genetic Programming Framework for Symbolic Regression
GP Evolutionary Dynamics
- Burlacu, K. Yang, M. Affenzeller - Evolutionary Dynamics in Genetic Programming for Symbolic
Regression
Population diversity videos:
2019
GPEM Special Issue - Integrating Numerical Optimization Methods with Genetic Programming
- Kommenda, B. Burlacu, G. Kronberger, M. Affenzeller - Parameter Identification for Symbolic Regression using Nonlinear Least Squares, Genetic Programming and Evolvable Machines, 2019, Springer
Experimental results: Results.csv
GECCO 2019
The Genetic and Evolutionary Computation Conference 13th to 17th July, 2019, Prague, Czech Republic
Parsimony Measures in Multi-objective Genetic Programming for Symbolic Regression
Poster Paper
Full Paper (not accepted)
Aggregated Results (Excel)
2018
Applied Artificial Intelligence Journal
- Fechter, A. Beham, S. Wagner and M. Affenzeller - Approximate Q-Learning for Stacking Problems with Continuous Production and Retrieval, Applied Artificial Intelligence, 2018, DOI: 10.1080/08839514.2018.1525852 link
Test Instances:
Instance file format description
Instance 1.1
Instance 1.2
Instance 1.3
Instance 2.1
Instance 2.2
Instance 2.3
Instance 3.1
Instance 3.2
Instance 3.3
Source code implemented in HeuristicLab:
GECCO 2018
The Genetic and Evolutionary Computation Conference 15th to 19th July, 2018, Kyoto, Japan
Algorithm Selection on Generalized Quadratic Assignment Problem Landscapes
Problem Instances
Algorithm Selection Data
Schema-based Diversification in Genetic Programming
HeuristicLab Build
OSGP Experiment
OSGPS-S Experiments
Applied Soft Computing
- Kronberger, M. Kommenda, E. Lughofer, S. Saminger-Platz, A. Promberger, F. Nickel, S. Winkler, M. Affenzeller - Robust Generalized Fuzzy Modeling and Enhanced Symbolic Regression for Modeling Tribological Systems, Applied Soft Computing, 2018 link
Data sets:
README.txt
CF1.csv
CF2.csv
CF3.csv
CF4.csv
NvhRating.csv
Temp1.csv
Temp2.csv
Wear1.csv
Wear2.csv
2017
IJSPM 2017
International Journal of Simulation and Process Modelling
Novel Robustness Measures for Engineering Design Optimisation
Result Tables
2016
Annals of Operations Research
- Vonolfen, M. Affenzeller - Distribution of waiting time for dynamic pickup and delivery problems, Annals of Operations Rsearch, Volume 236, Issue 2, pp 359 - 382, 2018 link
The following additional material is provided:
Test Environment (Executable including Sample)
Source code of the waiting strategies
Benchmark Instances
International Journal of Simulation and Process Modelling
S.M. Winkler, B. Castaño, S. Luengo, S. Schaller, G. Kronberger, M. Affenzeller - Heterogeneous model ensembles for short-term prediction of stock market trends, International Journal of Simulation and Process Modelling, 11(6), pp 504-513, 2016 link
Data set containing preprocessed time series of Spanish stocks: spanish_stock_data_3class.csv
EMSS 2016
28th European Modeling & Simulation Symposium, 26th to 28th September, 2016, Larnaca, Cyprus
Analysis of Uncertainty in Engineering Design Optimization Problems
Problem Implementation
PressureVessel.hl
SpeedReducer.hl
TensionCompressionSpring.hl
WeldedBeam.hl
Solved Problems Experiment
Result Tables
2015
EMSS 2015
27th European Modeling & Simulation Symposium, 21st to 23rd September, 2015, Bergeggi, Italy
Modelling a Clustered Generalized Quadratic Assignment Problem
Problem Instances
10-50-38.txt
10-50-51.txt
10-50-77.txt
15-35-45.txt
15-35-61.txt
15-35-91.txt
20-30-45.txt
20-30-61.txt
20-30-91.txt
2014
IEEE Transactions on Industrial Informatics
- Hutterer, A. Beham, M. Affenzeller, F. Auinger and S. Wagner - Software-Enabled Investigation in Metaheuristic Power Grid Optimization, IEEE Transactions on Industrial Informatics, Volume 10, No. 1, pp. 364-372, Feb. 2014 doi: 10.1109/TII.2013.2276525 link
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
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
Symbolic Regression Dataset: ppsn2014.csv
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
- Kommenda, G. Kronberger, S. Winkler, M. Affenzeller and S. Wagner - Effects of constant optimization by nonlinear least squares minimization in symbolic regression, Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, ACM, 2013 link
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 12 years ago.
- breiman-I.png (7.0 KB) - added by gkronber 12 years ago.
- breiman-I-variables.png (6.9 KB) - added by gkronber 12 years ago.
- friedman-ii.png (8.1 KB) - added by gkronber 12 years ago.
Download all attachments as: .zip