Hierarchical Approach to Evolutionary Multi-Objective Optimization

Authors:
Eryk Ciepiela;Joanna Kocot;Leszek Siwik;Rafał Dreżewski
Affiliations:
Department of Computer Science, AGH University of Science and Technology, Kraków, Poland;Department of Computer Science, AGH University of Science and Technology, Kraków, Poland;Department of Computer Science, AGH University of Science and Technology, Kraków, Poland;Department of Computer Science, AGH University of Science and Technology, Kraków, Poland
Venue:
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Year:
2008

Citing 3
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)

Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a new "hierarchical" evolutionary approach to solving multi-objective optimization problems is introduced. The results of experiments with standard multi-objective test problems, which were aimed at comparing "hierarchical" and "classical" versions of multi-objective evolutionary algorithms, show that the proposed approach is a very promising technique.