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A Multi-objective Genetic Algorithm with Relative Distance: Method, Performance Measures and Constraint Handling
Kolkata, India March 05-March 07
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCTA.2007.13International Conference on Computing ...
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Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, Sankar Kumar Pal, "A Multi-objective Genetic Algorithm with Relative Distance: Method, Performance Measures and Constraint Handling," International Conference on Computing: Theory and Applications, pp. 315-319, International Conference on Computing: Theory and Applications (ICCTA'07), 2007.
 
 
BibTex
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@article{ 10.1109/ICCTA.2007.13,
author = {Praveen Kumar Tripathi and Sanghamitra Bandyopadhyay and Sankar Kumar Pal},
title = {A Multi-objective Genetic Algorithm with Relative Distance: Method, Performance Measures and Constraint Handling},
journal ={International Conference on Computing: Theory and Applications},
volume = {0},
year = {2007},
isbn = {0-7695-2770-1},
pages = {315-319},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICCTA.2007.13},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Conference on Computing: Theory and Applications
TI - A Multi-objective Genetic Algorithm with Relative Distance: Method, Performance Measures and Constraint Handling
SN - 0-7695-2770-1
SP315
EP319
A1 - Praveen Kumar Tripathi,
A1 - Sanghamitra Bandyopadhyay,
A1 - Sankar Kumar Pal,
PY - 2007
KW - null
VL - 0
JA - International Conference on Computing: Theory and Applications
ER -
 
Praveen Kumar Tripathi, Indian Statistical Institute, India
Sanghamitra Bandyopadhyay, Indian Statistical Institute, India
Sankar Kumar Pal, Indian Statistical Institute, India
A novel Multi-Objective Evolutionary Algorithm (MOEA), called Multi-objective Genetic Algorithm with Relative Distance (MOGARD) is described. A novel relative distance parameter that ensures convergence to the Pareto optimal front and a nearest neighbour based method for maintaining diversity in the non-dominated set is used. Two novel performance measures are formulated to estimate the performance of the MOEAs. A penalty based constraint handling concept is introduced in MOGARD, for handling constraints. Experimental results demonstrate the superiority of MOGARD on several test problems, as compared to other recent and well known algorithms.
Citation:
Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, Sankar Kumar Pal, "A Multi-objective Genetic Algorithm with Relative Distance: Method, Performance Measures and Constraint Handling," iccta, pp.315-319, International Conference on Computing: Theory and Applications (ICCTA'07), 2007
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