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Genetic multi-step search in interpolation and extrapolation domain

Published: 07 July 2007 Publication History

Abstract

The deterministic Multi-step Crossover Fusion (dMSXF) is an improved crossover method of MSXF which is a promising method of JSP, and it shows high availability in TSP. Both of these crossover methods introduce a neighborhood structure and distance in each permutation problem and perform multi-step searches in the interpolation domain focusing on inheritance of parents' characteristic. They cannot work effectively when parents stand close each other since they search in interpolation domain. Therefore in the case of the MSXF, the Multi-step Mutation Fusion (MSMF), which is the multi-step search in the extrapolation domain, is combined as the supplementary search to improve its search performance. On the other hand, the search mechanism for acquisition of characteristics, such as MSMF, is not applied to dMSXF. In this paper, we introduce a deterministic MSMF (dMSMF) mechanism as complementary multi-step extrapolation search. We apply dMSXF+dMSMF to TSP and JSP, which have structural difference between their landscapes. Through the experiments it was shown that the deterministic multi-step search in interpolation/extrapolation domain performed effectively in combinatorial problems.

References

[1]
Sakuma, J., Kobayashi, S.: Extrapolation-Directed Crossover for Job-shop Scheduling Problems: Complementary Combination with JOX, Proc. of GECCO 2000, pp. 973--980 (2000).
[2]
Kobayashi, S., Ono, I. and Yamamura, M.: An Efficient Genetic Algorithm for Job Shop Scheduling Problems, Proc. of 6th Int. Conf. on Genetic Algorithms, pp. 506--511 (1995).
[3]
Yamamura, M., Ono, I. and Kobayashi, S.: Emergent Search on Double Circle TSPs using Subtour Exchange Crossvoer, Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 535--540 (1996).
[4]
Shi, G., Sannomiya, N.: A New Encoding Scheme for Solving Job Shop Problem by Genetic Algorithm, Proc. of 35th IEEE Conf. on Decision and Control, pp. 4395--4400 (1996).
[5]
Ono, I., Kobayashi, S.: A Genetic Algorithm Taking Account of Characteristics Preservation for Job Shop Scheduling Problems, Proc. of the International Conference on Intelligent Autonomous Systems 5, pp. 711--718 (1998).
[6]
Nagata, Y.: New EAX crossover for large TSP instances, Proc. of Parallel Problem Solving fron Nature, PPSN IX, pp. 372--381 (2006).
[7]
Ikeda, K., Kobayashi, S.: Deterministic Multi-step Crossover Fusion: A Handy Crossover for GAs, Proc. of Parallel Problem Solving fron Nature, PPSN VII, pp. 162--171 (2002).
[8]
Yamada, T. and Nakano, R.: Scheduling by Genetic Local Search with Multi-Step Crossover, Proc. of Parallel Problem Solving fron Nature, PPSN IV, pp. 960--969 (1996).
[9]
Boese, K. D.: Cost Versus Distance in the Traveling Salesman Problem, Technical report, TR--950018, UCLA CS Department (1995).
[10]
Ikeda, K., Kobayashi, S.: GA Based on the UV-Structure Hypothesis and Its Application to JSP, Proc. of Parallel Problem Solving fron Nature, PPSN VI, pp. 273--282 (2000).
[11]
Yagiura, M., Ibaraki, T.: Genetic and Local Search Algorithms as Robust and Simple Optimization Tools, Kluwer Academic Publishers, MA, USA (1996).
[12]
Aiex, R. M., Binato, S. and Resende, M. G. C.: Parallel GRASP with path--relinking for job shop scheduling, Parallel Computing, Vol. 29, pp. 393--430 (2003).
[13]
Glover, F.: Genetic algorithms and scatter search: unsuspected potentials, Statistics and Computing 4, pp. 131--140 (1994).
[14]
Marti, R., Laguna, M. and Glover, F.: Principles of Scatter Search, European Journal of Operational Research, Vol. 169 (2), pp. 359--372 (2006).
[15]
Giffler, B., Thompson, G.: Algorithms for Solving Production Scheduling Problems, Operations Research, Vol. 8, pp. 487--503 (1960).
[16]
Aarts, E., van Laarhoven, P., Lenstra, J., Ulder, N: A Computational Study of Local Search Algorithms for Job--shop Scheduling, ORSA J. on Comput, Vol. 6, No. 2, pp. 118--125 (1994).
[17]
Nowicki, E. and Smutnicki, C.: A Fast Taboo Search Algorithm for the Job--shop Scheduling Problem, Management Science, Vol. 42, No. 6, pp. 797--813 (1996).

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  • (2023)Optimization Algorithm with Automatic Adjustment of the Number of Switches in the Order/Radix ProblemIEICE Transactions on Information and Systems10.1587/transinf.2023PAP0004E106.D:12(1979-1987)Online publication date: 1-Dec-2023
  • (2020)Edge Assembly Crossover with Tabu for Traveling Salesman Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185837(1-6)Online publication date: Jul-2020
  • (2019)A New Probabilistic Tree Expression for Probabilistic Model Building Genetic ProgrammingComputational Science/Intelligence and Applied Informatics10.1007/978-3-030-25225-0_9(121-132)Online publication date: 26-Jul-2019
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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    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 ACM 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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    Published: 07 July 2007

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    Author Tags

    1. combinatorial optimization
    2. genetic algorithm
    3. local search

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2023)Optimization Algorithm with Automatic Adjustment of the Number of Switches in the Order/Radix ProblemIEICE Transactions on Information and Systems10.1587/transinf.2023PAP0004E106.D:12(1979-1987)Online publication date: 1-Dec-2023
    • (2020)Edge Assembly Crossover with Tabu for Traveling Salesman Problem2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185837(1-6)Online publication date: Jul-2020
    • (2019)A New Probabilistic Tree Expression for Probabilistic Model Building Genetic ProgrammingComputational Science/Intelligence and Applied Informatics10.1007/978-3-030-25225-0_9(121-132)Online publication date: 26-Jul-2019
    • (2018)Effectiveness of Multi-step Crossover in Extrapolation Domain for Genetic Programming2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00618(3654-3659)Online publication date: Oct-2018
    • (2018)Edge Assembly Crossover using Multiple Parents for Traveling Salesman Problem2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS)10.1109/SCIS-ISIS.2018.00088(474-477)Online publication date: Dec-2018
    • (2017)Probabilistic Model-Based Multistep Crossover Considering Dependency Between Nodes in Tree OptimizationSoftware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing10.1007/978-3-319-62048-0_13(187-200)Online publication date: 24-Jun-2017
    • (2016)Probabilistic Model-Based Multistep Crossover for Genetic Programming2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS)10.1109/SCIS-ISIS.2016.0043(154-159)Online publication date: Aug-2016
    • (2015)A Study on Neighborhood and Temperature in Multi-step Crossover Fusion for Tree StructureProceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 210.1007/978-3-319-13356-0_41(519-531)Online publication date: 2015
    • (2012)Effectiveness of Multi-step Crossover Fusions in genetic programming2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256564(1-8)Online publication date: Jun-2012
    • (2011)Effectiveness of genetic multistep searches in interpolation and extrapolation domains on multiobjective optimization2011 IEEE International Conference on Systems, Man, and Cybernetics10.1109/ICSMC.2011.6083716(669-674)Online publication date: Oct-2011
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