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Learning Enabled Constrained Black-Box Optimization Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Multi-Objective Evolutionary Algorithms: Past, Present, and Future

verfasst von : Carlos A. Coello Coello, Silvia González Brambila, Josué Figueroa Gamboa, Ma. Guadalupe Castillo Tapia

Erschienen in: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Verlag: Springer International Publishing

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Abstract

Evolutionary algorithms have become a popular choice for solving highly complex multi-objective optimization problems in recent years. Multi-objective evolutionary algorithms were originally proposed in the mid-1980s, but it was until the mid-1990s when they started to attract interest from researchers. Today, we have a wide variety of algorithms, and research in this area has become highly specialized. This chapter attempts to provide a general overview of multi-objective evolutionary algorithms, starting from their early origins, then moving in chronological order towards some of the most recent algorithmic developments. In the last part of the chapter, some future research paths on this topic are briefly discussed.

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Vorheriges Kapitel Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization
Nächstes Kapitel Black-Box and Data-Driven Computation
Fußnoten
1
The first author maintains the EMOO repository, which currently contains over 12,400 bibliographic references related to evolutionary multi-objective optimization. The EMOO repository is located at: https://​emoo.​cs.​cinvestav.​mx.
 
2
Without loss of generality, we will assume only minimization problems.
 
3
It is worth indicating that indicator-based archiving was introduced earlier (see [78, 79]).
 
5
NSGA-III was designed to solve many-objective optimization problems and its use is relatively popular today.
 
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Metadaten
Titel
Multi-Objective Evolutionary Algorithms: Past, Present, and Future
verfasst von
Carlos A. Coello Coello
Silvia González Brambila
Josué Figueroa Gamboa
Ma. Guadalupe Castillo Tapia
Copyright-Jahr
2021
Buch
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Print ISBN: 978-3-030-66514-2

Electronic ISBN: 978-3-030-66515-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-66515-9_5

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