NDLI: Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX

Content Provider	IEEE Xplore Digital Library
Author	Teo, J. Hijazi, H.A. Omar, Z.A. Mohamad, R. Hamid, Y.
Copyright Year	2007
Description	Author affiliation: Univ. Malaysia Sabah, Kota Kinabalu (Teo, J.)
Abstract	The objective of this paper is to implement a multi- pronged strategy for generating diversity using non-adaptive, adaptive as well as self-adaptive methods for controlling mutation operations in a real-coded genetic algorithm (RCGA). Currently, one of the state-of-the-art RCGAs for function optimization is called the G3-PCX algorithm. However, its performance for solving multimodal problems is known to be poor compared with its performance for unimodal problems. In G3-PCX, the main problem primarily stems from premature convergence to local rather than global optima due to lack of explorative capabilities of the algorithm. As the G3-PCX algorithm relies completely on crossover for promoting diversity, this paper proposes a multilevel mutation operator to augment the algorithm's capability of escaping local optima. The proposed algorithm is called G3M2 (G3-PCX with Multilevel Mutation) and empirical tests on four benchmark multimodal test functions have shown highly competitive performance. The objective of this paper is to investigate whether the proposed multilevel mutation is able to improve the precision accuracy of G3-PCX in solving multimodal function optimization problems. In three of the four problems, G3M2 outperformed the standard G3-PCX algorithm in terms of solution quality. Thus, the multilevel combination of non-adaptive, adaptive and self-adaptive parameter control strategies into a single paradigm is empirically shown to have beneficial effects for enhancing the effectiveness of the G3-PCX algorithm for solving multimodal optimization problems in terms of solution quality.
Starting Page	4502
Ending Page	4507
File Size	250912
Page Count	6
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4425061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation
Content Type	Text
Resource Type	Article

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

Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms

Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation

Improving the diversity preservation of multi-objective approaches used for single-objective optimization

Enhancing diversity for NSGA-II in evolutionary multi-objective optimization

Diversity improvement in Decomposition-Based Multi-Objective Evolutionary Algorithm for many-objective optimization problems

Diversity preservation with hybrid recombination for evolutionary multiobjective optimization

Harnessing Phenotypic Diversity towards Multiple Independent Objectives

Many-objective test problems with multiple Pareto optimal regions in a decision space

A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming

Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX

Similar Documents

Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms

Improving ANN classification accuracy for the identification of students with LDs through evolutionary computation

Improving the diversity preservation of multi-objective approaches used for single-objective optimization

Enhancing diversity for NSGA-II in evolutionary multi-objective optimization

Diversity improvement in Decomposition-Based Multi-Objective Evolutionary Algorithm for many-objective optimization problems

Diversity preservation with hybrid recombination for evolutionary multiobjective optimization

Harnessing Phenotypic Diversity towards Multiple Independent Objectives

Many-objective test problems with multiple Pareto optimal regions in a decision space

A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming

Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX