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![Evolutionary Multi-Task Optimization: Foundations and Methodologies](/uploads/posts/2023-03/1680189039_9451__volutionary_multi_task_optimization__foundations_and_m_thodologi_s.jpg)
Автор: Liang Feng, Abhishek Gupta, Kay Chen Tan
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2023
Страниц: 220
Язык: английский
Формат: pdf (true), epub
Размер: 38.5 MB
A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In Machine Learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain’s ability to generalize in optimization – particularly in population-based evolutionary algorithms – have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. Optimization is an essential ingredient in many real-world problem solving systems and Artificial Intelligence (AI) algorithms.