<ul><li>Researchers propose a novel evolutionary algorithm for optimizing real-valued objective functions on the Grassmann manifold.</li><li>Existing optimization techniques on the Grassmann manifold primarily rely on first- or second-order Riemannian methods, which struggle with nonconvex or multimodal landscapes.</li><li>The proposed algorithm adapts the Differential Evolution algorithm, a global, population-based optimization method, for effective operation on the Grassmann manifold.</li><li>The algorithm incorporates adaptive control parameter schemes and introduces a projection mechanism to maintain feasibility with manifold structure and explore beyond local neighborhoods.</li></ul>

Differential Evolution for Grassmann Manifold Optimization: A Projection Approach

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