Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

1. Khaled Rasheed. GADO: A genetic algorithm for continuous design optimization. Technical Report DCS-TR-352, Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ, January 1998. Ph.D. Thesis, http://webster.cs.uga.edu/~khaled/thesis.ps.

   Google Scholar 

2. Khaled Rasheed and Haym Hirsh. Learning to be selective in genetic-algorithm-based design optimization. Artificial Intelligence in Engineering, Design, Analysis and Manufacturing, 13:157–169, 1999.

   Google Scholar 

3. Deb, K., S. Agrawal, A. Pratap, and T. Meyarivan (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of the Parallel Problem Solving from Nature VI, pp.849–858.

   Google Scholar 

4. Khaled Rasheed and Haym Hirsh. Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pp. 628–635, 2000.

   Google Scholar 

5. K. Rasheed., S. Vattam, X. Ni. Comparison of Methods for Using Reduced Models to Speed up Design Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pp. 1180–1187, 2002.

   Google Scholar 

6. Khaled Rasheed. An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization. In Proceedings of the Congress on Evolutionary Computation (CEC’2002), pp. 986–993, 2002.

   Google Scholar 

7. K. Rasheed., S. Vattam, X. Ni. Comparison of methods for developing dynamic reduced models for design optimization. In Proceedings of the Congress on Evolutionary Computation (CEC’2002), pp. 390–395, 2002.

   Google Scholar 

8. William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes in C: the Art of Scientific Computing. Cambridge University Press, Cambridge [England]; New York, 2nd edition, 1992.

   Google Scholar 

9. J.D. Schaffer. Multi-objective optimization with vector evaluated genetic algorithms. In Proceedings of an International Conference on Genetic Algorithms and Their Applications, J.J. Grefenstette, Ed., Pittsburg, PA, July 24–26 1985, pp. 93–100, sponsored by Texas Instruments and U.S. Navy Center for Applied Research in Artificial Intelligence (NCARAI).

   Google Scholar 

10. Binh and Korn. MOBES: A multi-objective Evolution Strategy for constrained optimization Problems. In Proceedings of the 3 ^rd International Conference on Genetic Algorithm MENDEL 1997, Brno, Czech Republic, pp.176–182.

    Google Scholar 

11. Srinivas, N. and Deb, K. (1995). Multi-Objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation (2), 221–248.

    Article  Google Scholar 

12. Tanaka, M. (1995). GA-based decision support system for multi-criteria, optimization. In Proceedings of the International Conference on Systems, Man and Cybernetics-2, pp. 1556–1561.

    Google Scholar 

13. Osycza, A. and Kundu, S. (1995). A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Structural Optimization (10). 94–99.

    Article  Google Scholar 

14. Deb, K. Pratap, A. and Moitra, S. (2000). Mechanical Component Design for Multiple Objectives Using Elitist Non-Dominated Sorting GA. KanGAL Report No. 200002.

    Google Scholar 

15. Ranjithan, S.R., S.K. Chetan, and H.K. Dakshina (2001). Constraint method-based evolutionary algorithm (CMEA) for multi-objective optimization. In E. Z. et al. (Ed.), Evolutionary Multi-Criteria Optimization 2001, Lecture Notes in Computer Science 1993, pp. 299–313. Springer-Verlag.

    Google Scholar 

16. Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland.

    Google Scholar 

17. Crone, D. W., Knowles, J.D., and Oates, M.J. (2000). The Pareto Envelope-based Selection Algorithm for Multi-objective Optimization. In Schoenauer, M., Deb, K., Rudolph, g., Yao, X., Luton, E., Merelo, J.J., and Schewfel, H.-P., editors, Proceedings of the Parallel Problem Solving from Nature VI Conference, pp.839–848, Paris, France. Springer. Lecture Notes in Computer Science No. 1917.

    Chapter  Google Scholar 

18. K. Deb. Multi-objective optimization using evolutionary algorithms. Chichester, UK: John Wiley, 2001.

    MATH  Google Scholar 

19. K. Deb. S. Gulati (2001). Design of truss-structures for minimum weight using genetic algorithms, In Journal of Finite Elements in Analysis and Design, pp.447–465, 2001.

    Google Scholar 

Download references