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European Conference on the Applications of Evolutionary Computation

EvoApplications 2011: Applications of Evolutionary Computation pp 1–11Cite as

Evolving L-Systems as an Intelligent Design Approach to Find Classes of Difficult-to-Solve Traveling Salesman Problem Instances

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6624))

Abstract

The technique of computationally analysing a program by searching for instances which causes the program to run in its worst-case time is examined. Concorde [2], the state-of-the-art Traveling Salesperson Problem (TSP) solver, is the program used to test our approach. We seed our evolutionary approach with a fractal instance of the TSP, defined by a Lindenmayer system at a fixed order. The evolutionary algorithm produced modifications to the L-System rules such that the instances of the modified L-System become increasingly much harder for Concorde to solve to optimality. In some cases, while still having the same size, the evolved instances required a computation time which was 30,000 times greater than what was needed to solve the original instance that seeded the search. The success of this case study shows the potential of Evolutionary Search to provide new test-case scenarios for algorithms and their software implementations.

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References

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Author information

Authors and Affiliations

  1. NICTA, Australian Technology Park, Level 5, 13 Garden Street, Eveleigh, NSW, 2015, Australia

    Farhan Ahammed

  2. School of Information Technologies, The University of Sydney, NSW, 2006, Australia

    Farhan Ahammed

  3. School of Electrical Engineering and Computer Science, The University of Newcastle, NSW, 2308, Australia

    Pablo Moscato

  4. Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine, The University of Newcastle, NSW, 2308, Australia

    Pablo Moscato

Authors
  1. Farhan Ahammed
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  2. Pablo Moscato
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  3. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  4. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. Knowledge Engineering Research Group, Aston University, Aston Triangle, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

  7. Departamento de Electrónica y Tecnología de los Computadores, Universidad de Granada, 18071, Grenada, Spain

    Juan J. Merelo

  8. Department of Mathematical Information Technology, University of Jyväskylä, 4th Floor, P.O. Box 25, 40014, Agora, Finland

    Ferrante Neri

  9. Lehrstuhl für Algorithm Engineering, Technische Universität Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Mike Preuss

  10. Fakultät Elektrotechnik und Informationstechnik, HTWK Leipzig, Postfach 30 11 66, 04251, Leipzig, Germany

    Hendrik Richter

  11. Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen S, Denmark

    Julian Togelius  & Georgios N. Yannakakis  & 

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Ahammed, F., Moscato, P. (2011). Evolving L-Systems as an Intelligent Design Approach to Find Classes of Difficult-to-Solve Traveling Salesman Problem Instances. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-20525-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20524-8

  • Online ISBN: 978-3-642-20525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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