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Evolutionary Multi-Task Optimization
Liang Feng, Yew Soon Ong, Kay Chen Tan, et al.
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Naturwissenschaften, Medizin, Informatik, Technik / Informatik
Beschreibung
Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.
This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
Kundenbewertungen
continuous optimization, knowledge learning, large-scale optimization, combinatorial optimization, optimization, knowledge transfer, evolutionary computation, artificial intelligence