Meta-Lamarckian learning in memetic algorithms

IEEE Transactions on Evolutionary Computation

Volumn 8, Issue 2, 2004, Pages 99-110

  Ong, Yew Soon ^a   Keane, Andy J ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Adaptive Meta Lamarckian learning; Continuous parametric design optimization; Hybrid genetic algorithm local search (GA LS); Memetic algorithm (MA)

Indexed keywords

GENETIC ALGORITHMS; OPTIMIZATION; PROBABILITY; PROBLEM SOLVING; THEOREM PROVING;

CONTINUOUS PARAMETRIC DESIGN OPTIMIZATION; LOCAL SEARCH METHOD; MEMETIC ALGORITHMS; META-LAMARCKIAN LEARNING;

LEARNING ALGORITHMS;

EID: 2442474238     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2003.819944     Document Type: Article

References (37)

1. A. Torn and A. Zilinskas, Global Optimization. New York: Springer-Verlag, 1989, vol. 350, Lecture Notes in Computer Science.
2. P. Moscato, "On evolution, search, optimization, genetic algorithms and martial arts: toward memetic algorithms," Tech. Rep. Caltech Concurrent Computation Program, Rep. 826, California Inst. Technol., Pasadena, CA, 1989.
3. L. Davis, Handbook of Genetic Algorithms. New York: Van Nostrand, 1991.
4. W. E. Hart, "Adaptive Global Optimization With Local Search," Ph.D. dissertation, Univ. California, San Diego, CA, May 1994.
5. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolutionary Programs, 2nd ed. New York: Springer-Verlang, 1994.
6. H. Bersini and B. Renders, "Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways," in Proc. IEEE Int. Symp. Evolutionary Computation, Orlando, FL, 1994, pp. 312-317.
7. C. Houck, J. Joines, and M. Kay, "Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms," Meta-Heuristic Res. Appl. Group, Dept. Ind. Eng., North Carolina State Univ., NCSU-IE Tech. Rep. 96-01, 1996.
8. V. Vicini and D. Quagliarella, "Airfoil and wing design using hybrid optimization strategies," AIAA Journal, vol. 37, no. 5, pp. 634-641, May 1999.
9. P. Mez, "On the performance of memetic algorithms in combinatorial optimization," presented at the 2nd Workshop on Memetic Algorithms, GECCO 2001, San Francisco, CA.
10. N. Krasnogor, "Studies on the Theory and Design Space of Memetic Algorithms," Ph.D. dissertation , Faculty Comput., Math. Eng., Univ. West of England, Bristol, U.K., 2002.
11. N. Krasnogor, B. Blackburne, J. D. Hirts, and E. K. Burke, "Multimeme algorithms for the structure prediction and structure comparison of proteins," in Lecture Notes in Computer Science, Proc. Parallel Problem Solving From Nature, 2002.
12. Y. S. Ong and A. J. Keane, "A domain knowledge based search advisor for design problem solving environments," Eng. Appl. Artif. Intell., vol. 15, no. 1, pp. 105-116, 2002.
13. D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE Trans. Evol. Comput., vol. 1, pp. 67-82, Apr. 1997.
14. L. J. Fogel, P. J. Angeline, and D. B. Fogel, "A preliminary investigation on extending evolutionary programming to include self-adaptation on finite state machines," Informatica, vol. 18, pp. 387-398, 1995.
15. T. Back and M. Schutz, "Intelligent mutation rate control in canonical genetic algorithms," in Proc. 9th Int. Symp. (ISMIS '96), Zakopane, Poland, June 9-13, 1996, pp. 158-167.
16. R. Hinterding, Z. Michalewicz, and A. E. Eiben, "Adaptation in evolutionary computation: a survey," in Proc. 4th Int. Conf. Evolutionary Computation (ICEC 97), 1997, pp. 65-69.
17. J. E. Smith and T. C. Fogarty, "Operator and parameter adaptation in genetic algorithms," Soft Computing, vol. 1, no. 2, pp. 81-87, 1997.
18. G. Magyar, M. Johnsson, and O. Nevalainen, "An adaptive hybrid genetic algorithm for the three-matching problem," IEEE Trans. Evol. Comput., vol. 4, July 2000.
19. T.-M. Hugo, T.-M. Ross, T.-M. Peter, and V.-R. Manuel, "Evolution of constraint satisfaction strategies in examination timetabling," in Proc. Genetic Evolutionary Conf., Orlando, FL, July 13-17, 1999, pp. 635-642.
20. P. Cowling, G. Kendall, and E. Soubeiga, "A hyperheuristic approach to scheduling a sales summit," in 3rd Int. Conf. Practice and Theory of Automated Timetabling, (PATAT 2000) Lecture Notes in Computer Science. New York: Springer-Verlag.
21. D. W. Johnson and R. T. Johnson, Cooperation and Competition: Theory and Research. Edina, MN: Interaction Book Company, 1989.
22. F. G. Lobo and D.E. Goldberg, "Decision making in a hybrid genetic algorithm," in Proc. IEEE Int. Conf. Evolutionary Computation, 1997, pp. 122-125.
23. M. D. McKay, R. J. Beckman, and W. J. Conover, "A comparison of three methods for selecting values of input variables in the analysis of output from a computer code," Technometrics, vol. 21, no. 2, pp. 239-245, 1979.
24. W. M. Spears. (1991) A simple GA (conceptually based on genesis) written in C. [Online]. Available: http://www.aic.nrl.navy.mil:80/galist/src/#C
25. H. P. Schweferl, Evolution and Optimum Seeking. New York: Wiley, 1995.
26. J. N. Siddall, Optimal Engineering Design: Principles and Applications. New York: Marcel Dekker, 1982.
27. L. Davis, "Bit climbing, representational bias, and test suite design," in 4th International Conference on Genetic Algorithms (ICGA IV), R. K. Belew and L. B. Booker, Eds. San Diego, CA: Morgan Kaufman, 1991.
28. J. A. Nelder and R. Meade, "A simplex method for function minimization," Comput. J., vol. 7, pp. 308-313, 1965.
29. M. J. D. Powell, "An efficient method for finding the minimum of a function of several variables without calculating derivatives," Comput. J., vol. 7, no. 4, pp. 303-307, 1964.
30. M. J. D. De Jong, "An Analysis of the Behavior of a Class of Genetic Adaptive Systems," Ph.D. dissertation, Univ. Michigan, Ann Arbor, 1975.
31. A. J. Keane, "Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness," Artif. Intell. Eng., vol. 9, no. 2, pp. 75-83, 1995.
32. A. J. Keane, (1995) The OPTIONS Design Exploration Systems. [Online]. Available: http://www.soton.ac.uk/~ajk/options/welcome.html
33. C. Floudas and V. Visweswaran, "Quadratic optimization," in Handbook of Global Optimization, R. Horst and P. M. Pardalos, Eds. Norwell, MA: Kluwer, 1995.
34. M. J. D. Powell, "A direct search optimization method that models the objective function by linear interpolation," in Advances in Optimization and Numerical Analysis. Dordrecht, The Netherlands: Kluwer, 1994, pp. 51-57.
35. H. Voigt and J. Lange, "Local evolutionary search enhancement by random memorizing," in Proc. IEEE Int. Conf. Evolutionary Computation, Anchorage, AK, May 1998, pp. 547-552.
36. A. J. Keane and N. Petruzzelli, "Aircraft wing design using GA-based multi-level strategies," presented at the Proc. 8th AIAA/USAF/NASSA/ISSMO Symp. Multidisciplinary Analysis and Optimization, AIAA, Long Beach, CA, 2000.
37. J. Cousin and M. Metcalfe, "The BAE Ltd transport aircraft synthesis and optimization program," in AIAA 90-3295, Sept. 1990.