A Systematic Assessment of Genetic Algorithm (GA) in Optimizing Machine Learning Model: A Case Study from Building Science

A. Ali, R. Jayaraman, Elie Azar, A. Sleptchenko

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Machine learning (ML) algorithms are techniques that allow computers to learn from the data without being explicitly programmed. ML techniques consist of hyperparameters that typically influence prediction accuracy, hence requiring tuning. In this study, we systematically evaluate the performance of the genetic algorithm (GA) technique in tuning ML hyperparameters compared to three other common tuning techniques i.e. grid search (GS), random search (RS), and bayesian optimization (BO). While previous studies explored the potential of metaheuristics techniques such as GA in tuning ML models, a systematic comparison with other commonly mentioned techniques is currently lacking. Results indicate that GA slightly outperformed other methods in terms of optimality due to its ability to pick any continuous value within the range. However, apart from GS which took the longest, it was observed that GA is quite a time inefficient compared to RS and BO which were able to find a solution close to the GA within a shorter time (GA - 149 minutes, RS - 88 minutes, BO - 105 minutes, GS - 756 minutes).

    Original languageBritish English
    Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
    PublisherIEEE Computer Society
    Pages384-389
    Number of pages6
    ISBN (Electronic)9781665486873
    DOIs
    StatePublished - 2022
    Event2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia
    Duration: 7 Dec 202210 Dec 2022

    Publication series

    NameIEEE International Conference on Industrial Engineering and Engineering Management
    Volume2022-December
    ISSN (Print)2157-3611
    ISSN (Electronic)2157-362X

    Conference

    Conference2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
    Country/TerritoryMalaysia
    CityKuala Lumpur
    Period7/12/2210/12/22

    Keywords

    • Genetic algorithm
    • Hyperparameter
    • Machine learning

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