Evolutionary multi-objective optimization without additional parameters

Studies in Computational Intelligence

Volumn 54, Issue , 2007, Pages 241-257

  Deb, Kalyanmoy ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34147182930     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-69432-8_12     Document Type: Article

References (43)

1. T. P. Bagchi and K. Deb. Calibration of GA parameters: The design of experiments approach. Computer Science and Informatics, 26(4):46-56, 1996.
2. C. A. Coello Coello, A. H. Aguirre, and E. Zitzler, editors. Evolutionary Multi-Criterion Optimization: Third International Conference. Berlin, Germany: Springer, 2005. LNCS 3410.
3. K. Deb. IEEE TEC Paper (2002, vol. 6, pp. 182-197) on NSGA-II: Fast breaking paper in the field of engineering. ISI Web of Science, http://esitopics.com/fbp/2004/february04-KalyanmoyDeb.html.
4. K. Deb. Multi-objective evolutionary optimization: Past, present and future. In I. C. Parmee, editor, Evolutionary Design and Manufacture pages 225-236. London: Springer, 2000.
5. K. Deb. Multi-objective optimization using evolutionary algorithms Chichester, UK: Wiley, 2001.
6. K. Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Systems, 9(2):115-148, 1995.
7. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of the Parallel Problem Solving from Nature VI (PPSN-VI), pages 849-858, 2000.
8. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, 2002.
9. K. Deb, A. Anand, and D. Joshi. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation Journal, 10(4):371-395, 2002.
10. K. Deb, S. Chaudhuri, and K. Meitinnen. I-EMO: An interactive evolutionary multi-objective optimization tool. Technical Report KanGAL Report Number 2005005, Department of Mechanical Engineering, IIT Kanpur, India, 2005. Also in Proceedings of Pattern Recognition in Machine Intelligence (PReMI'05), Springer.
11. K. Deb and D. E. Goldberg. An investigation of niche and species formation in genetic function optimization. In Proceedings of the Third International Conference on Genetic Algorithms, pages 42-50, 1989.
12. K. Deb, M. Mohan, and S. Mishra. Towards a quick computation of well-spread pareto-optimal solutions. In Proceedings of the Second Evolutionary Multi-Criterion Optimization (EMO-03) Conference (LNCS 2632), pages 222-236, 2003.
13. K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceeedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), in press.
14. K. Deb and S. Tiwari. Omni-optimizer: A generic evolutionary algorithm for global optimization. European Journal of Operations Research (EJOR), in press.
15. L. J. Eshelman. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In Foundations of Genetic Algorithms 1 (FOGA-1), pages 265-283, 1991.
16. L. J. Eshelman and J. D. Schaffer. Real-coded genetic algorithms and intervalschemata. In Foundations of Genetic Algorithms 2 (FOGA-2), pages 187-202, 1993.
17. C. Fonseca, P. Fleming, E. Zitzler, K. Deb, and L. Thiele. Proceedings of the Second Evolutionary Multi-Criterion Optimization (EMO-03) Conference (Lecture Notes in Computer Science (LNCS) 2632). Heidelberg: Springer, 2003.
18. C. M. Fonseca and P. J. Fleming. An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation Journal, 3(1):1-16, 1995.
19. D. E. Goldberg. Genetic Algorithms for Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
20. D. E. Goldberg and K. Deb. A comparison of selection schemes used in genetic algorithms. In Foundations of Genetic Algorithms 1 (FOGA-1 pages 69-93, 1991.
21. D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6(4):333-362, 1992.
22. D. E. Goldberg, K. Deb, and D. Thierens. Toward a better understanding of mixing in genetic algorithms. Journal of the Society of Instruments and Control Engineers (SICE), 32(1):10-16, 1993.
23. D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multi-modal function optimization. In Proceedings of the First International Conference on Genetic Algorithms and Their Applications pages 41-49, 1987.
24. G. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation Journal, 7(3):231-254, 1999.
25. T. Higuchi, S. Tsutsui, and M. Yamamura. Theoretical analysis of simplex crossover for real-coded genetic algorithms. In Parallel Problem Solving from Nature (PPSN-VI), pages 365-374, 2000.
26. J. Horn, N. Nafploitis, and D.E. Goldberg. A niched Pareto genetic algorithm for multi-objective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 82-87, 1994.
27. K.A. De Jong and J. Sharma. Generation gap revisited. In Foundations of Genetic Algorithms (FOGA-II), pages 19-28, 1992.
28. James Kennedy and Russell C. Eberhart. Swarm intelligence. Morgan Kaufmann, 2001.
29. H. Kita, I. Ono, and S. Kobayashi. Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms. In Proceedings of the 1999 Congress on Evolutionary Computation, pages 1581-1587, 1999.
30. Joshua D. Knowles and David W. Corne. Approximating the non-dominated front using the Pareto archived evolution strategy. Evolutionary Computation Journal, 8(2):149-172, 2000.
31. S. Kukkonen and K. Deb. Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In Proceedings of the Congress on Evolutionary Computation (CEC-06), in press.
32. N. J. Radcliffe. Forma analysis and random respectful recombination. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 222-229, 1991.
33. C. R. Reeves. Using genetic algorithms with small populations. In Proceedings of the Fifth International Conference on Genetic Algorithms pages 92-99, 1993.
34. H. Satoh, M. Yamamura, and S. Kobayashi. Minimal generation gap model for gas considering both exploration and exploitation. In Proceedings of the IIZUKA: Methodologies for the Conception, Design, and Application of Intelligent Systems, pages 494-497, 1996.
35. J. D. Schaffer, R. A. Caruana, L. J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms. In Proceedings of the International Conference on Genetic Algorithms pages 51-60, 1989.
36. H.-P. Schwefel. Evolution and Optimum Seeking. New York: Wiley, 1995.
37. N. Srinivas and K. Deb. Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation Journal, 2(3):221-248, 1994.
38. R. Storn and K. Price. Differential evolution - A fast and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11:341-359, 1997.
39. H.-M. Voigt, H. Mühlenbein, and D. Cvetković. Fuzzy recombination for the Breeder Genetic Algorithm. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 104-111, 1995.
40. I. Wegener and T. Jansen. Real royal road functions - where crossover provably is essential. Discrete Applied Mathematics, 149:111-125, 2005.
41. D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82, 1977.
42. E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In K. C. Giannakoglou, D. T. Tsahalis, J. Périaux, K. D. Papailiou, and T. Fogarty, editors, Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pages 95-100, Athens, Greece, 2001. International Center for Numerical Methods in Engineering (Cmine).
43. E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257-271, 1999.