research-article

DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems

Authors:

Gjorgjina Cenikj,

Gašper Petelin,

Peter Korošec,

Tome EftimovAuthors Info & Claims

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 813 - 821

https://doi.org/10.1145/3583131.3590401

Published: 12 July 2023 Publication History

Abstract

The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to select or to configure a suitable algorithm for the problem at hand. Since in pure black-box optimization information about the problem instance can only be obtained through function evaluation, a common approach is to dedicate some function evaluations for feature extraction, e.g., using random sampling. This approach has two key downsides: (1) It reduces the budget left for the actual optimization phase, and (2) it neglects valuable information that could be obtained from a problem-solver interaction.

In this paper, we propose a feature extraction method that describes the trajectories of optimization algorithms using simple descriptive statistics. We evaluate the generated features for the task of classifying problem classes from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that the proposed DynamoRep features capture enough information to identify the problem class on which the optimization algorithm is running, achieving a mean classification accuracy of 95% across all experiments.

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Cited By

Renau QHart ELi XHandl J(2024)Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training SamplesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654025(1026-1035)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654025
Korošec PEftimov T(2024)Opt2Vec - a continuous optimization problem representation based on the algorithm's behaviorInformation Sciences: an International Journal10.1016/j.ins.2024.121134680:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121134
Renau QHart E(2024)Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-SelectionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_5(70-86)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_5
Show More Cited By

Index Terms

DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Learning latent representations
2. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

July 2023

1667 pages

ISBN:9798400701191

DOI:10.1145/3583131

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Copyright © 2023 ACM.

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Publication History

Published: 12 July 2023

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Slovenian Research Agency
French National Research Agency

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GECCO '23

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GECCO '23: Genetic and Evolutionary Computation Conference

July 15 - 19, 2023

Lisbon, Portugal

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Renau QHart ELi XHandl J(2024)Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training SamplesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654025(1026-1035)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654025
Korošec PEftimov T(2024)Opt2Vec - a continuous optimization problem representation based on the algorithm's behaviorInformation Sciences: an International Journal10.1016/j.ins.2024.121134680:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121134
Renau QHart E(2024)Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-SelectionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_5(70-86)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_5
Renau QHart E(2024)On the Utility of Probing Trajectories for Algorithm-SelectionApplications of Evolutionary Computation10.1007/978-3-031-56852-7_7(98-114)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_7

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