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Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10197))

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Abstract

In order to increase the performance of an evolutionary algorithm, additional auxiliary optimization objectives may be added. It is hard to predict which auxiliary objectives will be the most efficient at different stages of optimization. Thus, the problem of dynamic selection between auxiliary objectives appears. This paper proposes a new method for efficient selection of auxiliary objectives, which uses fitness landscape information and problem meta-features. An offline learned meta-classifier is used to dynamically predict the most efficient auxiliary objective during the main optimization run performed by an evolutionary algorithm. An empirical evaluation on two benchmark combinatorial optimization problems (Traveling Salesman and Job Shop Scheduling problems) shows that the proposed approach outperforms similar known methods of auxiliary objective selection.

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Notes

  1. 1.

    https://bitbucket.org/BASSIN/2017-olhp-tsp-jssp/src.

  2. 2.

    http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95.

  3. 3.

    http://watchmaker.uncommons.org.

  4. 4.

    http://www.cs.waikato.ac.nz/ml/weka.

  5. 5.

    http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/RandomForest.html.

  6. 6.

    http://people.brunel.ac.uk/~mastjjb/jeb/.

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Author information

Authors and Affiliations

  1. ITMO University, 49 Kronverksky Pr., Saint-Petersburg, Russia, 197101

    Anton Bassin & Arina Buzdalova

Authors
  1. Anton Bassin
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  2. Arina Buzdalova
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Corresponding author

Correspondence to Anton Bassin .

Editor information

Editors and Affiliations

  1. Austrian Institute of Technology, Vienna, Austria

    Bin Hu

  2. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

A Appendix: TSP and JSSP Instances Lists

A Appendix: TSP and JSSP Instances Lists

TSP Train: att532, bays29, brazil58, ch130, d198, d493, eil101, gil262, gr120, gr202, gr24, gr431, hk48, kroA150, kroB200, kroE100, p654, pa561, pr136, pr264, rat575, rat99, si175, st70, ts225, u574, ulysses22. TSP Cross-validate: a280, att48, bayg29, bays29, berlin52, bier127, brazil58, brg180, burma14, ch130, ch150, d198, d493, dantzig42, eil101, eil51, eil76, fl417, fri26, gil262, gr17, gr21, gr24, gr48, gr96, gr120, gr137, gr202, gr229, gr431, hk48, kroA100, kroA150, kroA200, kroB100, kroB150, kroB200, kroC100, kroD100, kroE100, lin105, lin318, pcb442, pr76, pr107, pr124, pr136, pr144, pr152, pr226, pr264, pr299, pr439, rat195, rat99, rd100, rd400, si175, st70, swiss42, ts225, tsp225, u159, ulysses16, ulysses22.

JSSP Train and Cross-validate: abz5, abz8, ft10, la02, la04, la07, la13, la15, la18, la19, la23, la24, la27, la28, la32, la33, la34, la37, la39, orb01, orb04, orb05, orb08, swv02, swv05, swv06, swv10, swv13, swv18, swv19, yn2, yn3.

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Bassin, A., Buzdalova, A. (2017). Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier. In: Hu, B., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017. Lecture Notes in Computer Science(), vol 10197. Springer, Cham. https://doi.org/10.1007/978-3-319-55453-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-55453-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55452-5

  • Online ISBN: 978-3-319-55453-2

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