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HeuristicLab is an open-source software system, where optimization and data analysis questions can be processed and solved with metaheuristic algorithms.
HeuristicLab has been developed by the Research Group "Heuristic and Evolutionary Algorithms Laboratory" (HEAL) for more than 15 years at the Faculty of Informatics, Communication and Media of the University of Applied Sciences Upper Austria.
It has been used successfully in research and industrial projects as well as in teaching.
HeuristicLab is characterized by a flexible architecture that allows the implementation and analysis of a wide range of algorithms to a variety of optimization problems from different spheres (for example production, logistics, medical and bioinformatics, mechatronics, finance) with the help of a graphical user interface.
FH-Prof. Priv.-Doz. DI Dr. Michael Affenzeller
Implementation of heuristic optimization techniques to solve complex optimization problems out of different spheres
Methods & Expertise for Research Infrastructure
Use of heuristic algorithms to solve complex optimization problems in production and logistics as well as in data-based non-linear modeling
Kepler University Hospital Linz
University of Vienna
This project aims to develop novel algorithms in order to gain additional optimization potential by modeling and optimizing interrelated logistics and production processes in an integrative way.
Efficient utilization of resources is essential for companies in order to offer products and services in a competitive and sustainable manner. Therefore, optimization is a key-technology in manifold domains to solve complex practical problems. A severe limitation of current optimization techniques is the difficulty of sufficiently formalizing the entire problem situation and complexity. Many existing problem models are abstracted and isolated formulations of real world situations in order to make existing optimization techniques applicable. Consequently, the optimized solutions are often hard to transfer into the real world as the inherent complexity and volatility of the problem situations have been lost.
The main goals for the application of optimization networks in this project are:
Integrated storage, transport, and schedule optimization
Strategic planning and design of production and logistics systems
Integration of data-based modeling in the optimization of production processes
May 2014- April 2018
RISC Software GmbH
A. Scheibenpflug, A. Beham, M. Kommenda, J. Karder, S. Wagner, M. Affenzeller. Simplifying Problem Definitions in the HeuristicLab Optimization Environment. Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO '15 Companion, ACM. 2015. (URL: http://dl.acm.org/citation.cfm?id=2768463)
A. Beham, J. Karder, G. Kronberger, S. Wagner, M. Kommenda, A. Scheibenpflug. Scripting and Framework Integration in Heuristic Optimization Environments. Companion Publication of the 2014 Genetic and Evolutionary Computation Conference, GECCO '14 Companion, ACM. 2014. (URL: https://dl.acm.org/citation.cfm?id=2598394.2605690)
S. Wagner et al. Architecture and Design of the HeuristicLab Optimization Environment. In Advanced Methods and Applications in Computational Intelligence, Topics in Intelligent Engineering and Informatics Series, Springer, pp. 197-261. 2014. (URL: http://link.springer.com/chapter/10.1007/978-3-319-01436-4_10)
G. Kronberger, M. Kommenda, S. Wagner, H. Dobler. GPDL: A Framework-Independent Problem Definition Language for Grammar Guided Genetic Programming. Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, ACM, pp. 1333-1340. 2013. (URL: http://dl.acm.org/citation.cfm?id=2482713&dl=ACM&coll=DL)
M. Kommenda, G. Kronberger, S. Wagner, S. M. Winkler, M. Affenzeller. On the Architecture and Implementation of Tree-based Genetic Programming in HeuristicLab. GECCO '12 Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, ACM, pp. 101-108. 2012. (URL: http://dl.acm.org/citation.cfm?id=2330784.2330801&coll=DL&dl=GUIDE)
A. Beham et al. Integration of Flexible Interfaces in Optimization Software Frameworks for Simulation-Based Optimization. GECCO '12 Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, ACM, pp. 125-132. 2012. (URL: http://dl.acm.org/citation.cfm?id=2330804)
A. Scheibenpflug, S. Wagner, E. Pitzer, M. Affenzeller. Optimization Knowledge Base: An Open Database for Algorithm and Problem Characteristics and Optimization Results. GECCO '12 Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, ACM, pp. 141-148. 2012. (URL: http://dl.acm.org/citation.cfm?id=2330784.2330806)
S. Wagner, G. Kronberger, A. Beham, S. Winkler, M. Affenzeller. Model Driven Rapid Prototyping of Heuristic Optimization Algorithms. Computer Aided Systems Theory - EUROCAST 2009, vol. 5717, Springer, pp. 729–736. 2009. (URL: http://link.springer.com/chapter/10.1007%2F978-3-642-04772-5_94)
S. Wagner et al. Benefits of Plugin-Based Heuristic Optimization Software Systems. Computer Aided Systems Theory - EUROCAST 2007, vol. 4739, Springer, pp. 747–754. 2007. (URL: http://link.springer.com/chapter/10.1007/978-3-540-75867-9_94)