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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Learning Heuristics With Different Representations for Stochastic Routing.

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    This study explored numeric representations for hyperheuristic routing policies, comparing them to traditional tree representations. While tree representations performed best, artificial neural network (ANN) representations showed competitive results for stochastic routing problems.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Computer Science

    Background:

    • Uncertainty is a key challenge in real-world routing problems.
    • Hyperheuristic methods offer automated policy design for dynamic and stochastic routing.
    • Genetic programming with tree representations is a common, flexible approach.

    Purpose of the Study:

    • To investigate the efficacy of numeric representations for routing policies.
    • To compare linear and artificial neural network (ANN) representations against tree representations.
    • To analyze the optimization characteristics and data requirements of numeric representations.

    Main Methods:

    • Implemented and compared linear and ANN representations with tree representations.
    • Evaluated policies on a stochastic routing problem and an uncertain capacitated arc routing problem.
    • Utilized hyperheuristic methods for automated routing policy design.

    Main Results:

    • Tree representation generally yielded the best performance.
    • ANN representations achieved competitive performance on most test instances.
    • ANN policies require more training data compared to tree representations.

    Conclusions:

    • Numeric representations, particularly ANN, offer a viable alternative to tree representations for routing policies.
    • Representation selection depends on problem specifics and available training data.
    • Further research into ANN optimization for routing is warranted.