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    Area of Science:

    • Artificial Intelligence
    • Optimization
    • Evolutionary Computation

    Background:

    • Humans excel at pattern recognition and knowledge reuse for efficient problem-solving.
    • Artificial intelligence (AI) systems can benefit from mimicking these cognitive abilities, especially in speed-critical applications.
    • Evolutionary multitasking, using a single population to solve multiple optimization tasks, shows promise but struggles with uncontrolled intertask transfer.

    Purpose of the Study:

    • To develop a cognizant evolutionary multitasking engine for multiobjective optimization.
    • To enable the algorithm to learn and adapt intertask relationships and genetic transfer online.
    • To address the challenge of negative transfer in evolutionary multitasking without prior knowledge of task relatedness.

    Main Methods:

    • The proposed algorithm analyzes overlaps in probabilistic search distributions generated during multitasking.
    • It dynamically adjusts the extent of genetic transfer based on learned intertask relationships.
    • The method is evaluated on standard multiobjective benchmark problems and a real-world case study.

    Main Results:

    • The engine successfully learns intertask relationships from data generated during multitasking.
    • Online adaptation of genetic transfer effectively mitigates negative transfer.
    • Significant performance speedups were observed, particularly in knowledge transfer from low-fidelity to high-fidelity optimization tasks.

    Conclusions:

    • The cognizant evolutionary multitasking engine offers an adaptive and principled approach to intertask transfer.
    • This method enhances efficiency and accuracy in solving multiple optimization problems.
    • It demonstrates practical applicability by reducing the cost of high-fidelity optimization through learned knowledge transfer.