NDLI: A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms

Content Provider	ACM Digital Library
Author	Aleti, Aldeida Moser, Irene
Copyright Year	2016
Description	Author Affiliation: Faculty of Information Technology, Monash University, VIC, Australia (Aleti, Aldeida); Faculty of Science, Engineering 8 Technology, Swinburne University of Technology, Victoria, Australia (Moser, Irene)
Abstract	Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm. In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.
Starting Page	1
Ending Page	35
Page Count	35
File Format	PDF
ISSN	03600300
e-ISSN	15577341
DOI	10.1145/2996355
Journal	ACM Computing Surveys (CSUR)
Volume Number	49
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolutionary algorithms Adaptive parameter control
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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