Evolutionary Multi-Task Optimization

丛 书 名：Machine Learning: Foundations, Methodologies, and Applications

版本说明：1

作     者：Liang Feng Abhishek Gupta Kay Chen Tan Yew Soon Ong 

I S B N：(纸本) 9789811956492;9789811956522 

出 版 社：Springer Singapore 

出 版 年：1000年

页      数：X, 219页

主 题 词：Artificial Intelligence Machine Learning Optimization Computational Intelligence 

摘      要：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.