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Updated: Apr 25, 2026

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Learning Evolution Via Optimization Knowledge Adaptation.

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    Summary
    This summary is machine-generated.

    This study introduces the Optimization Knowledge Adaptation Evolutionary Model (OKAEM), a novel framework for evolutionary algorithms (EAs). OKAEM effectively unifies knowledge transfer and online adaptation, outperforming existing methods in various scenarios.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Evolutionary Computation

    Background:

    • Evolutionary algorithms (EAs) store optimization knowledge in historical data.
    • Current methods isolate knowledge transfer and online adaptation, limiting effectiveness.
    • Existing approaches struggle with complete knowledge utilization and operator-specific adaptations.

    Purpose of the Study:

    • To develop a unified framework for evolutionary algorithms that simultaneously enables knowledge transfer and online adaptation.
    • To introduce the Optimization Knowledge Adaptation Evolutionary Model (OKAEM) for adaptive parameter updating.
    • To leverage optimization knowledge for improved EA performance.

    Main Methods:

    • Introduced the Optimization Knowledge Adaptation Evolutionary Model (OKAEM), a learnable evolutionary framework.
    • Parameterized evolutionary operators using attention mechanisms for adaptive updates.
    • Implemented a two-phase approach: pre-training for knowledge transfer and adaptive optimization for real-time tuning.

    Main Results:

    • OKAEM significantly outperformed state-of-the-art sequential transfer methods in 12 transfer scenarios via pre-training.
    • OKAEM surpassed advanced learnable EAs in prior-free settings using its self-tuning mechanism.
    • Demonstrated practical utility in prompt tuning for vision-language models.

    Conclusions:

    • OKAEM effectively integrates and utilizes optimization knowledge for both transfer and adaptation.
    • The model's learnable components and adaptive mechanisms are crucial for its superior performance.
    • OKAEM autonomously discovers interpretable evolutionary principles, offering insights into EA behavior.