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Multi-species Conserved Sequences02:51

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Updated: Dec 17, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential

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    Evolutionary multitasking (EMT) solves multiple optimization tasks simultaneously, but poor knowledge transfer can hinder performance. A new algorithm, MOMFEA-SADE, improves knowledge transfer using subspace alignment and self-adaptive differential evolution, enhancing optimization results.

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    Area of Science:

    • Artificial Intelligence
    • Computational Intelligence
    • Optimization Algorithms

    Background:

    • Traditional single-tasking evolutionary algorithms optimize one problem at a time.
    • Evolutionary multitasking (EMT) addresses multiple optimization tasks concurrently, leveraging knowledge sharing for improved performance.
    • Ineffective knowledge transfer in EMT can lead to suboptimal results.

    Purpose of the Study:

    • To propose a novel multiobjective evolutionary multitasking algorithm (MOMFEA-SADE) that enhances knowledge transfer quality.
    • To improve the performance of evolutionary multitasking by mitigating negative knowledge transfer between tasks.
    • To introduce a method combining subspace alignment and self-adaptive differential evolution for advanced EMT.

    Main Methods:

    • Developed MOMFEA-SADE, a multiobjective EMT algorithm incorporating subspace alignment and self-adaptive differential evolution (DE).
    • Utilized a mapping matrix derived from subspace learning to transform search spaces and minimize negative knowledge transfer.
    • Integrated a self-adaptive trial vector generation strategy within DE to create effective solutions based on prior experience.

    Main Results:

    • Experimental results demonstrated MOMFEA-SADE's superior or comparable performance against existing state-of-the-art EMT algorithms.
    • MOMFEA-SADE achieved success in the Competition on Evolutionary Multitask Optimization at IEEE 2019 Congress on Evolutionary Computation.
    • The algorithm effectively reduced negative knowledge transfer through subspace alignment.

    Conclusions:

    • MOMFEA-SADE offers an effective approach to enhance knowledge transfer in evolutionary multitasking.
    • The proposed method shows significant potential for improving the efficiency and effectiveness of solving multiple optimization tasks simultaneously.
    • The algorithm's success in a competitive setting validates its advanced capabilities in multiobjective multi/many-tasking optimization.