Title :
NEMO: neural enhancement for multiobjective optimization
Author :
Garrett, Aaron ; Dozier, Gerry ; Deb, Kalyanmoy
Author_Institution :
Jacksonville State Univ., Jacksonville
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;
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
DOI :
10.1109/CEC.2007.4424868
