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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Optimization

    Background:

    • Modern cloud applications generate vast amounts of data and models, challenging existing transfer evolutionary optimization (TrEO) frameworks.
    • Current TrEO methods struggle with scalability to numerous source tasks and online learning agility when relevant sources are sparse, limiting practical deployment.
    • Negative transfer remains a significant concern in large-scale transfer optimization scenarios.

    Purpose of the Study:

    • To develop a novel TrEO framework capable of handling a significantly larger number of source tasks (beyond 1000).
    • To enhance online learning agility by effectively managing sparse relevant source tasks.
    • To enable practical deployment of transfer optimization in big task instance scenarios while mitigating negative transfer.

    Main Methods:

    • Devised a novel TrEO framework with two co-evolving species for joint evolution in source knowledge and solution search spaces.
    • Implemented co-evolution to orchestrate learned knowledge dynamically, accelerating convergence on the target optimization task.
    • Conducted extensive experiments on discrete and continuous optimization problems with numerous source tasks, including sparsely related ones.

    Main Results:

    • The proposed framework demonstrated efficient scalability with a growing number of source tasks, exceeding two orders of magnitude improvement.
    • Effectively captured relevant knowledge even with a small fraction of related sources, addressing sparsity.
    • Achieved significant improvements in both scalability and online learning agility compared to existing TrEO algorithms.

    Conclusions:

    • The novel TrEO framework successfully addresses the limitations of existing methods in handling large-scale transfer optimization.
    • The co-evolutionary approach enables efficient knowledge orchestration and rapid convergence in target tasks.
    • The framework is well-suited for practical deployment in big data scenarios, offering robust scalability and learning agility.