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Towards the design and implementation of optimization networks in HeuristicLab

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Michael AffenzellerAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1209 - 1214

https://doi.org/10.1145/3067695.3082475

Published: 15 July 2017 Publication History

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Abstract

Combining multiple algorithms to cooperate in solving different optimization problems or process other workflows can be done in various problem domains, e.g. combinatorial optimization and data analysis. Optimization networks allow us to create such cooperative approaches by connecting multiple algorithms and letting them work together. In this paper, we propose an optimization network architecture for HeuristicLab. Networks are built using nodes that perform arbitrary tasks. We introduce the concepts of messages and ports, which can be used to exchange data between nodes. The application of such optimization networks is shown for two different applications. One is to solve the Traveling Thief Problem, where we substitute parts of the original problem with subproblems that are optimized interdependently. In another scenario, feature selection is combined with linear regression to find the best combination of features in order to achieve the best linear regression model.

References

[1]

Michael Affenzeller and Stefan Wagner. 2005. Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms. In Adaptive and Natural Computing Algorithms (Springer Computer Series), B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, and N. C. Steele (Eds.). Springer, 218--221.

Google Scholar

[2]

David L Applegate, Robert E Bixby, Vasek Chvatal, and William J Cook. 2011. The traveling salesman problem: a computational study. Princeton university press.

Digital Library

Google Scholar

[3]

Andreas Beham, Judith Fechter, Michael Kommenda, Stefan Wagner, Stephan M Winkler, and Michael Affenzeller. 2015. Optimization Strategies for Integrated Knapsack and Traveling Salesman Problems. In International Conference on Computer Aided Systems Theory. Springer, 359--366.

Google Scholar

[4]

Mohammad Reza Bonyadi, Zbigniew Michalewicz, and Luigi Barone. 2013. The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In 2013 IEEE Congress on Evolutionary Computation. IEEE, 1037--1044.

Crossref

Google Scholar

[5]

Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.

Digital Library

Google Scholar

[6]

Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. 1995. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley.

Digital Library

Google Scholar

[7]

Brian W Goldman and William F Punch. 2014. Parameter-less population pyramid. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. ACM, 785--792.

Digital Library

Google Scholar

[8]

Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. Journal of machine learning research 3, Mar (2003), 1157--1182.

Digital Library

Google Scholar

[9]

Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos. 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation 11, 1 (2003), 1--18.

Digital Library

Google Scholar

[10]

John Neter, Michael H Kutner, Christopher J Nachtsheim, and William Wasserman. 1996. Applied linear statistical models. Vol. 4. Irwin Chicago.

Google Scholar

[11]

Sartaj Sahni. 1975. Approximate algorithms for the 0/1 knapsack problem. Journal of the ACM (JACM) 22, 1 (1975), 115--124.

Digital Library

Google Scholar

[12]

Stefan Wagner, Gabriel Kronberger, Andreas Beham, Michael Kommenda, Andreas Scheibenpflug, Erik Pitzer, Stefan Vonolfen, Monika Kofler, Stephan Winkler, Viktoria Dorfer, and Michael Affenzeller. 2014. Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, Vol. 6. Springer, Chapter Architecture and Design of the HeuristicLab Optimization Environment, 197--261. http://link.springer.com/chapter/10.1007/978-3-319-01436-4_10

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Cited By

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Karder JBeham AWerth BWagner SAffenzeller M(2022)Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service TimesProcedia Computer Science10.1016/j.procs.2022.01.302200(1031-1040)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.01.302
Çelik YFidan Ş(2022)Analysis of cutting parameters on tool wear in turning of Ti-6Al-4V alloy by multiple linear regression and genetic expression programming methodsMeasurement10.1016/j.measurement.2022.111638200(111638)Online publication date: Aug-2022
https://doi.org/10.1016/j.measurement.2022.111638
Karder JBeham APeirleitner AAltendorfer K(2020)Surrogate-Assisted Multi-Objective Parameter Optimization for Production Planning SystemsComputer Aided Systems Theory – EUROCAST 201910.1007/978-3-030-45093-9_29(239-246)Online publication date: 15-Apr-2020
https://doi.org/10.1007/978-3-030-45093-9_29
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Index Terms

Towards the design and implementation of optimization networks in HeuristicLab
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software architectures
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 15 July 2017

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Österreichische Forschungsförderungsgesellschaft

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Karder JBeham AWerth BWagner SAffenzeller M(2022)Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service TimesProcedia Computer Science10.1016/j.procs.2022.01.302200(1031-1040)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.01.302
Çelik YFidan Ş(2022)Analysis of cutting parameters on tool wear in turning of Ti-6Al-4V alloy by multiple linear regression and genetic expression programming methodsMeasurement10.1016/j.measurement.2022.111638200(111638)Online publication date: Aug-2022
https://doi.org/10.1016/j.measurement.2022.111638
Karder JBeham APeirleitner AAltendorfer K(2020)Surrogate-Assisted Multi-Objective Parameter Optimization for Production Planning SystemsComputer Aided Systems Theory – EUROCAST 201910.1007/978-3-030-45093-9_29(239-246)Online publication date: 15-Apr-2020
https://doi.org/10.1007/978-3-030-45093-9_29
Karder JBeham AWerth BWagner SAffenzeller MTakadama K(2018)Asynchronous surrogate-assisted optimization networksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208246(1266-1267)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208246
Kommenda MKarder JBeham ABurlacu BKronberger GWagner SAffenzeller M(2018)Optimization Networks for Integrated Machine LearningComputer Aided Systems Theory – EUROCAST 201710.1007/978-3-319-74718-7_47(392-399)Online publication date: 26-Jan-2018
https://doi.org/10.1007/978-3-319-74718-7_47
Karder JBeham AWagner SAffenzeller M(2018)Solving the Traveling Thief Problem Using Orchestration in Optimization NetworksComputer Aided Systems Theory – EUROCAST 201710.1007/978-3-319-74718-7_37(307-315)Online publication date: 26-Jan-2018
https://doi.org/10.1007/978-3-319-74718-7_37

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Grasshopper Optimisation Algorithm

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