ABSTRACT
Rapid prototyping and testing of new ideas has been a major argument for evolutionary computation frameworks. These frameworks facilitate the application of evolutionary computation and allow experimenting with new and modified algorithms and problems by building on existing, well tested code. However, one could argue, that despite the many frameworks of the metaheuristics community, software packages such as MATLAB, GNU Octave, Scilab, or RStudio are quite popular. These software packages however are associated more closely with numerical analysis rather than evolutionary computation. In contrast to typical evolutionary computation frameworks which provide standard implementations of algorithms and problems, these popular frameworks provide a direct programming environment for the user and several helpful functions and mathematical operations. The user does not need to use traditional development tools such as a compiler or linker, but can implement, execute, and visualize his ideas directly within the environment. HeuristicLab has become a popular environment for heuristic optimization over the years, but has not been recognized as a programming environment so far. In this article we will describe new scripting capabilities created in HeuristicLab and give information on technical details of the implementation. Additionally, we show how the programming interface can be used to integrate further metaheuristic optimization frameworks in HeuristicLab. Categories and Subject D.
- A. Beham, E. Pitzer, S. Wagner, M. Affenzeller, K. Altendorfer, T. Felberbauer, and M. Bäck. Integration of flexible interfaces in optimization software frameworks for simulation-based optimization. In Companion Publication of the 2012 Genetic and Evolutionary Computation Conference, GECCO'12 Companion, pages 125--132, Philadelphia, PA, USA, July 2012. Google ScholarDigital Library
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2):182--197, 2002. Google ScholarDigital Library
- F. Dobslaw. Input: The intelligent parameter utilization tool. In Companion Publication of the 2012 Genetic and Evolutionary Computation Conference, GECCO'12 Companion, pages 149--156, Philadelphia, PA, USA, July 2012. Google ScholarDigital Library
- J. J. Durillo and A. J. Nebro. jMetal: A java framework for multi-objective optimization. Advances in Engineering Software, 42:760--771, 2011. Google ScholarDigital Library
- F.-A. Fortin, F.-M. De Rainville, M.-A. Gardner, M. Parizeau, and C. Gagné. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13:2171--2175, jul 2012. Google ScholarDigital Library
- J. Knowles and D. Corne. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 1. IEEE, 1999.Google ScholarCross Ref
- A. J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J. J. Durillo, and A. Beham. Abyss: Adapting scatter search to multiobjective optimization. Evolutionary Computation, IEEE Transactions on, 12(4):439--457, 2008. Google ScholarDigital Library
- J. A. Parejo, A. Ruiz-Cortés, S. Lozano, and P. Fernandez. Metaheuristic optimization frameworks: a survey and benchmarking. Soft Computing, 16(3):527--561, 2012. Google ScholarDigital Library
- S. Voß and D. L. Woodruff. Optimization software class libraries. Springer, 2002.Google ScholarCross Ref
- S. Wagner. Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University, Linz, Austria, 2009.Google Scholar
- S. Wagner, G. Kronberger, A. Beham, M. Kommenda, A. Scheibenpflug, E. Pitzer, S. Vonolfen, M. Kofler, S. Winkler, V. Dorfer, and M. Affenzeller. Advanced Methods and Applications in Computational Intelligence, volume 6 of Topics in Intelligent Engineering and Informatics, chapter Architecture and Design of the HeuristicLab Optimization Environment, pages 197--261. Springer, 2014.Google Scholar
- E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In K. Giannakoglou et al., editors, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95--100. International Center for Numerical Methods in Engineering (CIMNE), 2002.Google Scholar
Index Terms
- Scripting and framework integration in heuristic optimization environments
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GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary ComputationSoftware frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already ...
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