On the effect of the steady-state selection scheme in multi-objective genetic algorithms

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

Volumn 5467 LNCS, Issue , 2010, Pages 183-197

  Durillo, Juan J ^a   Nebro, Antonio J ^a   Luna, Francisco ^a   Alba, Enrique ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Comparative study; Generational and steady state selection scheme; Genetic algorithms

Indexed keywords

APPROXIMATION SET; COMPARATIVE STUDIES; COMPLEX OPTIMIZATION; COMPUTING TIME; CONVERGENCE SPEED; MULTI OBJECTIVE; MULTI-OBJECTIVE GENETIC ALGORITHM; NSGA-II; PARETO FRONT; SELECTION SCHEME; SELECTION STRATEGIES; STATE SELECTION; STEADY-STATE MODELS; STEADY-STATE VERSION;

CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PROFITABILITY;

APPROXIMATION ALGORITHMS;

EID: 78650723945     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01020-0_18     Document Type: Conference Paper

References (21)

1. Chafekar, D., Xuan, J., Rasheed, K.: Constrained multi-objective optimization using steady state genetic algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 813-824. Springer, Heidelberg (2003)
2. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
3. Deb, K., Mohan, M., Mishar, S.: Evaluating the ε-Domination Based Multi- Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation 13(4), 501-525 (2005)
4. Deb, K., Mohan, M., Mishra, S.: Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 222-236. Springer, Heidelberg (2003)
5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197 (2002) (Pubitemid 34555372)
6. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, pp. 105-145. Springer, Heidelberg (2005)
7. Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7, 1-30 (2006)
8. Durillo, J.J., Nebro, A.J., Luna, F., Alba, E.: A Study of Master-Slave Approaches to Parallelize NSGA-II. In: IEEE Iinternational Symposium on Parallel and Distributed Processing, 2008 - IPDPS 2008, pp. 1-8 (2008)
9. Durillo, J.J., Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E.: jMetal: A Java Framework for Developing Multi-Objective Optimization Metaheuristics. Technical Report ITI-2006-10, Dept. de Lenguajes y Ciencias de la Computación, University of Málaga (December 2006)
10. Emmerich, M., Beume, N., Naujoks, B.: An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62-76. Springer, Heidelberg (2005)
11. Huband, S., Hingston, P., Barone, L., While, R.L.: A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Trans. Evolutionary Computation 10(5), 477-506 (2006) (Pubitemid 44560739)
12. Igel, C., Suttorp, T., Hansen, N.: Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 171-185. Springer, Heidelberg (2007)
13. Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. Technical Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2006)
14. Kumar, R., Rockett, P.: Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation 10(3), 283-314 (2002)
15. Nebro, A.J., Durillo, J.J., Coello, C.A., Luna, F., Alba, E.: A Study of Convergence Speed in Multi-objective Metaheuristics. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 171-185. Springer, Heidelberg (2008)
16. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Grefenstette, J.J. (ed.) Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93-100 (1985)
17. Srinivasan, D., Rachmawati, L.: An Efficient Multi-objective Evolutionary Algorithm with Steady-State Replacement Model. In: GECCO 2006, pp. 715-722 (2006)
18. Valenzuela, C.L.: A Simple Evolutionary Algorithm for Multi-Objective Optimization (SEAMO). In: IEEE Congress on Evolutionary Computation, 2002 - CEC 2002, pp. 717-722 (2002)
19. Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173-195 (2000)
20. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
21. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257-271 (1999) (Pubitemid 30544879)