- Title
- Memetic algorithms for business analytics and data science: a brief survey
- Creator
- Moscato, Pablo; Mathieson, Luke
- Relation
- ARC.FT120100060 http://purl.org/au-research/grants/arc/FT120100060
- Relation
- Business and Consumer Analytics: New Ideas p. 545-608
- Publisher Link
- http://dx.doi.org/10.1007/978-3-030-06222-4_13
- Publisher
- Springer Cham
- Resource Type
- book chapter
- Date
- 2019
- Description
- This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018.
- Subject
- artificial intelligence; data analytics; data clustering; machine learning; memetic alforithm
- Identifier
- http://hdl.handle.net/1959.13/1460502
- Identifier
- uon:45984
- Identifier
- ISBN:9783030062217
- Language
- eng
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