Overview
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.
The book, Evolutionary Multi-Task Optimization: Foundations and Methodologies (Machine Learning: Foundations, Methodologies, and Applications) [Bulk, Wholesale, Quantity] ISBN#9789811956492 in Hardcover by Liang Feng, Abhishek Gupta, Kay Chen Tan, Yew Soon Ong may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.
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