Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Name: Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research
Creator: Nirupam Chakraborti
Keywords: 2. Zero hunger, 0203 mechanical engineering, 0103 physical sciences, 02 engineering and technology, 01 natural sciences

Nirupam Chakraborti

Found an issue? Give us feedback

Materials Science an...arrow_drop_down

Materials Science and Technology

Article . 2014 . Peer-reviewed

License: https://journals.sagepub.com/page/policies/text-and-data-mining-license

Data sources: Crossref

https://dx.doi.org/10.1179/174...

Article

Data sources: Microsoft Academic Graph

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

descriptionPublicationkeyboard_double_arrow_right Article 01 Sep 2014 English Publisher:SAGE PublicationsJournal:Materials Science and Technology, volume 30, pages 1,259-1,262 (issn: 0267-0836, eissn: 1743-2847,

Copyright policy )

Authors: Nirupam Chakraborti;

doi: 10.1179/1743284714y.0000000578

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

- Summary
- Metrics

Abstract

The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses. The basic concepts of multi-objective optimisation and Pareto optimality are explained in simple terms and the advantages of an evolutionary approach are emphasised. Current materials related research in this area is summarised, focusing on the achievements to date and the specific needs for further improvement.

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	35
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%