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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization

This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations make...

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Bibliographic Details
Main Authors: Saxena, Dhish Kumar (Author), Mittal, Sukrit (Author), Deb, Kalyanmoy (Author), Goodman, Erik D. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edition:1st ed. 2024.
Series:Genetic and Evolutionary Computation,
Subjects:
Artificial intelligence.
Machine learning.
Computational intelligence.
Artificial Intelligence.
Machine Learning.
Computational Intelligence.
Online Access:https://doi.org/10.1007/978-981-99-2096-9
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Table of Contents:
  • Introduction
  • Optimization Problems and Algorithms
  • Existing Machine Learning Studies on Multi-objective Optimization
  • Learning to Converge Better and Faster
  • Learning to Diversify Better and Faster
  • Learning to Simultaneously Converge and Diversify Better and Faster
  • Learning to Understand the Problem Structure
  • ML-Assisted Analysis of Pareto-optimal Front
  • Further Machine Learning Assisted Enhancements
  • Conclusions.

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