Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014-11-15

AUTHORS

Carlos A. Coello Coello

ABSTRACT

This chapter provides a short overview of the most significant research work that has been conducted regarding the solution of computationally expensive multi-objective optimization problems. The approaches that are briefly discussed include problem approximation, function approximation (i.e., surrogates) and evolutionary approximation (i.e., clustering and fitness inheritance). Additionally, the use of alternative approaches such as cultural algorithms, small population sizes and hybrids that use a few solutions (generated with optimizers that sacrifice diversity for the sake of a faster convergence) to reconstruct the Pareto front with powerful local search engines are also briefly discussed. In the final part of the chapter, some topics that (from the author’s perspective) deserve more research, are provided. More... »

PAGES

3-18

Book

TITLE

Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

ISBN

978-3-319-11540-5
978-3-319-11541-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-11541-2_1

DOI

http://dx.doi.org/10.1007/978-3-319-11541-2_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045730276


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