Paper Info
Title
Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster
Abstract
In this paper, multi-objective evolutionary programming (MOEP) using fuzzy rank-sum with diversified selection is introduced. The performances of this algorithm as well as MOEP with non-domination sorting on the set of benchmark functions provided for CEC2009 Special Session and competition on Multi-objective Optimization are reported. With this rank-sum sorting and diversified selection, the speed of the algorithm has increased significantly, in particular by about twenty times on five objective problems when compared with the implementation using the non-domination sorting. Beside this, the proposed approach has performed either comparable or better than the MOEP with non-domination sorting.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983312
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
twenty time,benchmark function,multi-objective optimization,multi-objective evolutionary programming,cec2009 special session,objective problem,fuzzy rank-sum,diversified selection,evolutionary computation,random number generation,sorting,optimization,programming,genetic programming,multi objective optimization,evolutionary programming,fuzzy set theory,artificial intelligence,probability density function,data mining
Mathematical optimization,Computer science,Fuzzy logic,Evolutionary computation,Sorting,Fuzzy set,Genetic programming,Artificial intelligence,Random number generation,Evolutionary programming,Probability density function,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-2959-2
20
0.82
References 
Authors
7
2
Name
Order
Citations
PageRank
B. Y. Qu120311.67
P. N. Suganthan210876412.72