A hierarchical approach in distributed evolutionary algorithms for multiobjective optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4818 LNCS, Issue , 2008, Pages 516-523

  Zaharie, Daniela ^a   Petcu, Dana ^a   Panica, Silviu ^a  

^a WEST UNIVERSITY OF TIMISOARA   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL TESTS; COMPUTING CLUSTERS; CONFIGURABLE; DATA MINING TASKS; DISTRIBUTED EVOLUTIONARY ALGORITHMS; EXECUTION ENVIRONMENTS; GRID ENVIRONMENTS; GRID INFRASTRUCTURES; HIERARCHICAL APPROACH; LAYERED STRUCTURES; LOCAL CLUSTER; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; SINGLE COMPUTER;

COMPUTER SCIENCE; EVOLUTIONARY ALGORITHMS; GRID COMPUTING;

MULTIOBJECTIVE OPTIMIZATION;

EID: 70350255750     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78827-0_59     Document Type: Conference Paper

References (10)

1. Deb, K., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 181-197 (2002)
2. Coello, C.A., van Veldhuizen, D.A., Lamont, G.B.: Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, Dordrecht (2002)
3. Deb, K., Zope, P., Jain, A.: Distributed computing of Pareto-optimal solutions using multi-objective evolutionary algorithms. In: Fonseca, C.M., et al. (eds.) EMO 2003. LNCS, vol. 2632, pp. 535-549. Springer, Heidelberg (2003)
4. Hiroyasu, T., Miki, M., Watanabe, S.: The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm. In: Proc. of IEEE Congress on Evolutionary Computation (CEC 2000), vol. 1, pp. 333-340. IEEE Computer Society, Los Alamitos (2000)
5. Melab, N., Cahon, S., Talbi, E.G.: Grid computing for parallel bioinspired algorithms. J. Parallel Distrib. Comput. 66, 1052-1061 (2006)
6. Nebro, A.J., Alba, E., Luna, F.: Observations in using grid technologies for multi-objective optimization. In: Di Martino, B., et al. (eds.) Engineering the Grid, pp. 27-39 (2006)
7. Streichert, F., Ulmer, H., Zell, A.: Parallelization of multi-objective evolutionary algorithms using clustering algorithms. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 92-107. Springer, Heidelberg (2005)
8. Wang, L., Fu, X.: Data Mining with Computational Intelligence. Springer, Berlin (2005)
9. Zaharie, D., Petcu, D.: Adaptive Pareto differential evolution and its parallelization. In: Wyrzykowski, R., et al. (eds.) PPAM 2004. LNCS, vol. 3019, pp. 261-268. Springer, Heidelberg (2004)
10. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 8(2), 125-148 (2000)