Memetic algorithms for business analytics and data science: a brief survey

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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