Evolutionary Multi-Task Optimization : Foundations and Methodologies

2023 ed.

by Abhishek Gupta , Kay Chen Tan , Liang Feng , Yew Soon Ong

Book Details

Format Hardback or Cased Book

Book Series Machine Learning: Foundations, Methodologies, and Applications

ISBN-10 9811956499

ISBN-13 9789811956492

Edition 2023 ed.

Publisher Springer Verlag, Singapore

Imprint Springer Verlag, Singapore

Country of Manufacture GB

Country of Publication GB

Publication Date Mar 30th, 2023

Print length 219 Pages

Product Classification: Optimization Artificial intelligence Machine learning

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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.

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. 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.

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