Featured Articles
Evolutionary Multitask Optimization: Advanced Algorithms and Transformative Applications in Computational Science
This article provides a comprehensive exploration of Evolutionary Multitask Optimization (EMTO), an emerging paradigm in computational intelligence that enables the simultaneous solving of multiple optimization tasks through implicit parallelism and knowledge transfer. We examine the foundational principles of EMTO, starting from the pioneering Multifactorial Evolutionary Algorithm (MFEA) to contemporary hybrid models. The review systematically details innovative methodologies addressing critical challenges like negative transfer and premature convergence, including adaptive operator selection, domain adaptation techniques, and novel knowledge transfer mechanisms. Further, we present a rigorous analysis of validation frameworks and performance metrics used to benchmark EMTO algorithms, supplemented by compelling case studies of real-world applications spanning reservoir scheduling, combinatorial network optimization, and complex system design. This synthesis is tailored for researchers and computational professionals seeking to understand and apply EMTO to accelerate discovery in data-intensive domains including biomedical research and drug development.
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