20% off all books with the code: BOOKS
  • check 10+ million books
  • check New arrivals every day
  • check Trusted by 1M+ customers
  • check Great prices & discounts
  • check Shipping across Europe

Evolutionary Multi-Task Optimization: Foundations and Methodologies - Liang Feng,Kay Chen Tan,Yew Soon Ong,Abhishek Gupta

English
2023-03-30
€230.37 €287.96

-20% with code BOOKS

In stock at our supplier

Shipping in 17-23 days

30-day return policy

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 emula ... Full description

You May Also Like

Description

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.

More Information

Author Liang Feng, Kay Chen Tan, Yew Soon Ong, Abhishek Gupta
Publisher Springer Nature Singapore
Release year 2023
Cover type Hardcover
EAN 9789811956492
Write Your Own Review
You're reviewing: Evolutionary Multi-Task Optimization: Foundations and Methodologies
Your Rating:

Goodreads Reviews

€230.37 €287.96