research-article

Gradient search in the space of permutations: an application for the linear ordering problem

Authors:

Valentino Santucci,

Marco BaiolettiAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 1704 - 1711

https://doi.org/10.1145/3377929.3398094

Published: 08 July 2020 Publication History

Abstract

Gradient search is a classical technique for optimizing differentiable functions that has gained much relevance recently due to its application on Neural Network training. Despite its popularity, the application of gradient search has been limited to the continuous optimization and its usage in the combinatorial case is confined to a few works, all which tackle the binary search space. In this paper, we present a new approach for applying Gradient Search to the space of permutations. The idea consists of optimizing the expected objective value of a random variable defined over permutations. Such a random variable is distributed according to the Plackett-Luce model, and a gradient search over its continuous parameters is performed. Conducted experiments on a benchmark of the linear ordering problem confirm that the Gradient Search performs better than its counterpart Estimation of Distribution Algorithm: the Plackett-Luce EDA. Moreover, results reveal that the scalability of the Gradient Search is better than that of the PL-EDA.

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Cited By

Malagón MIrurozki ECeberio J(2024)A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative ModelsACM Transactions on Evolutionary Learning and Optimization10.1145/36656504:3(1-28)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3665650
Arza ECeberio JIrurozki EPérez A(2022)Comparing Two Samples Through Stochastic Dominance: A Graphical ApproachJournal of Computational and Graphical Statistics10.1080/10618600.2022.208440532:2(551-566)Online publication date: 19-Jul-2022
https://doi.org/10.1080/10618600.2022.2084405
Lugo LSegura CMiranda G(2022)A diversity-aware memetic algorithm for the linear ordering ProblemMemetic Computing10.1007/s12293-022-00378-514:4(395-409)Online publication date: 16-Oct-2022
https://doi.org/10.1007/s12293-022-00378-5
Show More Cited By

Index Terms

Gradient search in the space of permutations: an application for the linear ordering problem
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
      2. Randomized search

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cover image ACM Conferences

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Copyright © 2020 ACM.

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Published: 08 July 2020

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GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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Cited By

Malagón MIrurozki ECeberio J(2024)A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative ModelsACM Transactions on Evolutionary Learning and Optimization10.1145/36656504:3(1-28)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3665650
Arza ECeberio JIrurozki EPérez A(2022)Comparing Two Samples Through Stochastic Dominance: A Graphical ApproachJournal of Computational and Graphical Statistics10.1080/10618600.2022.208440532:2(551-566)Online publication date: 19-Jul-2022
https://doi.org/10.1080/10618600.2022.2084405
Lugo LSegura CMiranda G(2022)A diversity-aware memetic algorithm for the linear ordering ProblemMemetic Computing10.1007/s12293-022-00378-514:4(395-409)Online publication date: 16-Oct-2022
https://doi.org/10.1007/s12293-022-00378-5
Struminsky KGadetsky ARakitin DKarpushkin DVetrov DRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Leveraging recursive Gumbel-Max trick for approximate inference in combinatorial spacesProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541102(10999-11011)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541102

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