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Self-adapting genetic algorithms with an application to project scheduling

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  • Hartmann, Sönke

Abstract

This paper introduces a new general framework for genetic algorithms to solve a broad range of optimization problems. When designing a genetic algorithm, there may be several alternatives for a component such as crossover, mutation or decoding procedure, and it may be difficult to determine the best alternative (e.g., the best crossover strategy) a priori. For such cases, we suggest to include alternative components into the genetic algorithm. Indicating the component to be actually used in the genotype, this allows the genetic algorithm to adapt itself. That is, the genetic algorithm learns which of the alternative components is the most successful by means of genetic optimization. In order to demonstrate the potential of the self-adapting genetic algorithm concept, we apply it to the classical resource-constrained project scheduling problem (RCPSP). Motivated by previous computational studies as well as theoretical insight, we employ two different decoding procedures and leave the decision which of them to select to the evolution. The approach is further enhanced by a problem-specific local search extension. An in-depth computational experimental analysis shows that the self-adapting genetic algorithm approach is currently the most promising heuristic for the RCPSP.

Suggested Citation

  • Hartmann, Sönke, 1999. "Self-adapting genetic algorithms with an application to project scheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 506, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    • Handle: RePEc:zbw:cauman:506
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    References listed on IDEAS

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    1. Sprecher, Arno & Kolisch, Rainer & Drexl, Andreas, 1995. "Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 80(1), pages 94-102, January.
    2. Rainer Kolisch & Arno Sprecher & Andreas Drexl, 1995. "Characterization and Generation of a General Class of Resource-Constrained Project Scheduling Problems," Management Science, INFORMS, vol. 41(10), pages 1693-1703, October.
    3. Kolisch, Rainer, 1996. "Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation," European Journal of Operational Research, Elsevier, vol. 90(2), pages 320-333, April.
    4. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
    5. Kolisch, Rainer & Hartmann, Sönke, 1999. "Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 10966, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Schirmer, Andreas, 1998. "Case-based reasoning and improved adaptive search for project scheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 472, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    7. Aristide Mingozzi & Vittorio Maniezzo & Salvatore Ricciardelli & Lucio Bianco, 1998. "An Exact Algorithm for the Resource-Constrained Project Scheduling Problem Based on a New Mathematical Formulation," Management Science, INFORMS, vol. 44(5), pages 714-729, May.
    8. Erik L. Demeulemeester & Willy S. Herroelen, 1997. "New Benchmark Results for the Resource-Constrained Project Scheduling Problem," Management Science, INFORMS, vol. 43(11), pages 1485-1492, November.
    9. Sprecher, Arno, 1996. "Solving the RCPSP efficiently at modest memory requirements," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 425, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    10. James H. Patterson, 1984. "A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem," Management Science, INFORMS, vol. 30(7), pages 854-867, July.
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    Cited by:

    1. Kolisch, R. & Padman, R., 2001. "An integrated survey of deterministic project scheduling," Omega, Elsevier, vol. 29(3), pages 249-272, June.
    2. Edgar Gutiérrez Franco & Fernando La Torre Zurita & Gonzalo Mejía Delgadillo, 2007. "A genetic algorithm for the resource constrained project scheduling problem (RCPSP)," Investigación & Desarrollo 0307, Universidad Privada Boliviana, revised Mar 2007.

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