Recombination and Self-Adaptation in Multi-objective Genetic Algorithms

1. Corne, D.W., Knowles, J.D., Oates, M.J.: The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.P. (eds.) Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 839–848. Springer, Heidelberg (2000)

   Chapter  Google Scholar 

2. Costa, L., Oliveira, P.: An Evolution Strategy for Multiobjective Optimization. In: Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 97–102. IEEE Service Center, Piscataway (2002)

   Google Scholar 

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

   Google Scholar 

4. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailou, P., Fogarty, T. (eds.) EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, September 2001, pp. 12–21 (2001)

   Google Scholar 

5. Laumanns, M., Rudolph, G., Schwefel, H.P.: Approximating the Pareto Set: Concepts, Diversity Issues, and Performance Assessment, Technical Report CI-72/99, Dortmund: Department of Computer Science/LS11, University of Dortmund, Germany (1999) ISSN 1433-3325

   Google Scholar 

6. Laumanns, M., Rudolph, G., Schwefel, H.P.: Mutation Control and Convergence in Evolutionary Multi-Objective Optimization. In: Matousek, R., Osmera, P. (eds.) Proceedings of the 7th International Mendel Conference on Soft Computing (MENDEL 2001), Brno University of Technology, Brno, Czech Republic, pp. 97–106 (2001)

   Google Scholar 

7. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval schemata. In: Whitley, D. (ed.) Foundations of Genetic Algorithms II, pp. 187–202 (1993)

   Google Scholar 

8. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

   MATH  Google Scholar 

9. Deb, K., Beyer, H.G.: Self-Adaptation in Real-Parameter Genetic Algorithms with Simulated Binary Crossover. In: Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, FL (1999)

   Google Scholar 

10. Schlierkamp-Voosen, D., Mühlenbein, H.: Strategy Adaptation by Competing Subpopulations. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 199–208. Springer, Heidelberg (1994)

    Google Scholar 

11. Bäck, T.: Evolutionary algorithms in Theory and Practice. Oxford University Press, New York (1996)

    MATH  Google Scholar 

12. Spears, W.M.: Adapting crossover in evolutionary algorithms. In: Proceeding of the 5th Annual Conference on Evolutionary Programming, San Diego, CA, Morgan Kaufmann Publishers, San Francisco (1995)

    Google Scholar 

13. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)

    Article  Google Scholar 

14. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J.J. (ed.) Proccedings of the First International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, pp. 93–100 (1985)

    Google Scholar 

Download references