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  • AMGA: An Adaptive and Modular Genetic Algorithm for the Traveling Salesman Problem

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    Ohira221842.pdf (785.0Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Ohira, Ryoma
    Islam, Md Saiful
    Jo, Jun
    Stantic, Bela
    Griffith University Author(s)
    Islam, Saiful
    Jo, Jun
    Stantic, Bela
    Year published
    2019
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    Abstract
    The choice in selection, crossover and mutation operators can significantly impact the performance of a genetic algorithm (GA). It is found that the optimal combination of these operators are dependent on the problem characteristics and the size of the problem space. However, existing works disregard the above and focus only on introducing adaptiveness in one operator while having other operators static. With adaptive operator selection (AOS), this paper presents a novel framework for an adaptive and modular genetic algorithm (AMGA) to discover the optimal combination of the operators in each stage of the GA’s life in order ...
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    The choice in selection, crossover and mutation operators can significantly impact the performance of a genetic algorithm (GA). It is found that the optimal combination of these operators are dependent on the problem characteristics and the size of the problem space. However, existing works disregard the above and focus only on introducing adaptiveness in one operator while having other operators static. With adaptive operator selection (AOS), this paper presents a novel framework for an adaptive and modular genetic algorithm (AMGA) to discover the optimal combination of the operators in each stage of the GA’s life in order to avoid premature convergence. In AMGA, the selection operator changes in an online manner to adapt the selective pressure, while the best performing crossover and mutation operators are inherited by the offspring of each generation. Experimental results demonstrate that our AMGA framework is able to find the optimal combinations of the GA operators for each generation for different instances of the traveling salesman problem (TSP) and outperforms the existing AOS models.
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    Conference Title
    International Conference on Intelligent Systems Design and Applications
    Volume
    941
    DOI
    https://doi.org/10.1007/978-3-030-16660-1_107
    Copyright Statement
    © 2019 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Optimisation
    Computational complexity and computability
    Artificial life and complex adaptive systems
    Publication URI
    http://hdl.handle.net/10072/385137
    Collection
    • Conference outputs

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