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Evolutionary Multi-Task Optimization Foundations and Methodologies /

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 induc...

Deskribapen osoa

Gorde:
Xehetasun bibliografikoak
Egile Nagusiak: Feng, Liang (Egilea), Gupta, Abhishek (Egilea), Tan, Kay Chen (Egilea), Ong, Yew Soon (Egilea)
Erakunde egilea: SpringerLink (Online service)
Formatua: Baliabide elektronikoa eBook
Hizkuntza:ingelesa
Argitaratua: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edizioa:1st ed. 2023.
Saila:Machine Learning: Foundations, Methodologies, and Applications,
Gaiak:
Artificial intelligence.
Machine learning.
Mathematical optimization.
Computational intelligence.
Artificial Intelligence.
Machine Learning.
Optimization.
Computational Intelligence.
Sarrera elektronikoa:https://doi.org/10.1007/978-981-19-5650-8
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Aurkibidea:
  • Chapter 1.Introduction
  • Chapter 2. Overview and Application-driven Motivations of Evolutionary Multitasking
  • Chapter 3.The Multi-factorial Evolutionary Algorithm
  • Chapter 4. Multi-factorial Evolutionary Algorithm with Adaptive Knowledge Transfer
  • Chapter 5.Explicit Evolutionary Multi-task Optimization Algorithm
  • Chapter 6.Evolutionary Multi-task Optimization for Generalized Vehicle Routing Problem With Occasional Drivers
  • Chapter 7. Explicit Evolutionary Multi-task Optimization for Capacitated Vehicle Routing Problem
  • Chapter 8. Multi-Space Evolutionary Search for Large Scale Single-Objective Optimization
  • Chapter 9.Multi-Space Evolutionary Search for Large-scale Multi-Objective Optimization.

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