DocumentCode
2693868
Title
NEMO: neural enhancement for multiobjective optimization
Author
Garrett, Aaron ; Dozier, Gerry ; Deb, Kalyanmoy
Author_Institution
Jacksonville State Univ., Jacksonville
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
3108
Lastpage
3113
Abstract
In this paper, a neural network approach is presented to expand the Pareto-optimal front for multiobjective optimization problems. The network is trained using results obtained from the nondominated sorting genetic algorithm (NSGA-II) on a set of well-known benchmark multiobjective problems. Its performance is evaluated against NSGA-II, and the neural network is shown to perform extremely well. Using the same number of function evaluations, the neural network produces many times more non-dominated solutions than NSGA-II.
Keywords
Pareto optimisation; genetic algorithms; neural nets; Pareto-optimal front; multiobjective optimization; neural network; nondominated sorting genetic algorithm; Constraint optimization; Evolutionary computation; Genetic algorithms; Neural networks; Pareto optimization; Particle swarm optimization; Performance evaluation; Sorting;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location
Singapore
Print_ISBN
978-1-4244-1339-3
Electronic_ISBN
978-1-4244-1340-9
Type
conf
DOI
10.1109/CEC.2007.4424868
Filename
4424868
Link To Document