NDLI: Adaptive-mutation compact genetic algorithm for dynamic environments

Content Provider	Springer Nature Link
Author	Uzor, Chigozirim J. Gongora, Mario Coupland, Simon Passow, Benjamin N.
Copyright Year	2016
Abstract	In recent years, the interest in studying nature-inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyperselection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs, but requires that the mutation factors be picked a priori and this is one of the limitations of the hypermutation scheme. This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results show that the change trend and mutation schemes have an impact on the performance of the amcGA in dynamic environment and also indicate that the effect of the schemes depends on the dynamics of the environment as well as the dynamic problem being considered.
Starting Page	3097
Ending Page	3115
Page Count	19
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	8
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-06-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dynamic optimization problems (DOPs) Evolutionary algorithms (EAs) Compact genetic algorithm (cGA) Adaptive-mutation compact genetic algorithm (amcGA) Population-based incremental learning (PBIL) Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes

Best practices in measuring algorithm performance for dynamic optimization problems

On the applicability of diploid genetic algorithms in dynamic environments

Prediction in evolutionary algorithms for dynamic environments

Using an adaptive collection of local evolutionary algorithms for multi-modal problems

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Community detection in complex networks using extended compact genetic algorithm

Corpus-based recombinant composition using a genetic algorithm

EASEA: specification and execution of evolutionary algorithms on GPGPU

Adaptive-mutation compact genetic algorithm for dynamic environments

Similar Documents

Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes

Best practices in measuring algorithm performance for dynamic optimization problems

On the applicability of diploid genetic algorithms in dynamic environments

Prediction in evolutionary algorithms for dynamic environments

Using an adaptive collection of local evolutionary algorithms for multi-modal problems

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Community detection in complex networks using extended compact genetic algorithm

Corpus-based recombinant composition using a genetic algorithm

EASEA: specification and execution of evolutionary algorithms on GPGPU

Adaptive-mutation compact genetic algorithm for dynamic environments