Landscape classification and problem specific reasoning for genetic algorithms

Kybernetes

Volumn 34, Issue 9-10, 2005, Pages 1469-1495

  Mac Giolla Bhride F ^a   McGinnity, T M ^a   McDaid, L J ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Artificial intelligence; Cybernetics; Programming and algorithm theory

Indexed keywords

ARTIFICIAL INTELLIGENCE; CYBERNETICS; ECONOMIC AND SOCIAL EFFECTS; INTELLIGENT SYSTEMS;

CROSS-OVER PROBABILITY; DESIGN/METHODOLOGY/APPROACH; EFFECTIVE PARAMETERS; HYBRID INTELLIGENT SYSTEM; INTELLIGENT OPERATOR; NO FREE LUNCH THEOREM; PRE-MATURE CONVERGENCES; PROGRAMMING AND ALGORITHM THEORY;

GENETIC ALGORITHMS;

EID: 27644432305     PISSN: 0368492X     EISSN: None     Source Type: Journal    
DOI: 10.1108/03684920510614777     Document Type: Article

References (49)

1. Angeline, P.J. (1995), "Two self-adaptive crossover operations for genetic programming", Advances in Genetic Programming II, MIT Press.
2. Bäck, T. (1992a), "The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm", Parallel Problem Solving from Nature 2, pp. 87-96, PPSN-II.
3. Bäck, T. (1992b), "Self-adaptation in genetic algorithms", Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, MIT Press, Cambridge, MA, pp. 263-71.
4. Bäck, T. (1996), Evolutionary Algorithms in Theory and Practice, Oxford University Press, Oxford.
5. Darwin, C. (1859), The Origin of Species, Oxford University Press, Oxford (reissue edition 1998).
6. Davis, L. (1989), "Adapting operator probabilities in genetic algorithms", Proceedings of the Third International Conference on Genetic Algorithms (ICGA III), pp. 61-9.
7. Davis, L. (1991), Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY.
8. Deb, K. and Beyer, H.G. (1999), "Self-adaptation in real-parameter genetic algorithms with simulated binary crossover", Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, FL, Vol. 1, pp. 172-9.
9. DeJong, K. (1975), "An analysis of the behavior of a class of genetic adaptive systems", PhD dissertation, San Mateo, CA.
10. DeJong, K.A. (1985), "Genetic algorithms: a 10 year perspective", Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 169-77.
11. DeJong, K.A. and Spears, W.M. (1991), "An analysis of the interacting roles of population size and crossover in genetic algorithms", Parallel Problem Solving from Nature - Proceedings of 1st Workshop, {PPSN} 1, 496, Springer, Berlin, pp. 38-47.
12. Eiben, A.E., Hinterding, R. and Michalewicz, Z. (2000), "Parameter control in evolutionary algorithms", IEEE Transactions on Evolutionary Computation, Vol. 3 No. 2, pp. 124-41.
13. Eiben, A.E., Sprinkhuizen-Kuyper, I.G. and Thijssen, B.A. (1998), "Competing crossovers in an adaptive GA framework", IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 787-92.
14. Espinoza, F.P., Minsker, B.S. and Goldberg, D.E. (2001), "A self adaptive hybrid genetic algorithm", Proceedings of the Genetic Evolutionary Computation Conference (GECCO 2001), Morgan Kaufmann, San Francisco, CA.
15. Fogarty, T.C. (1989), "Varying the probability of mutation in the genetic algorithm", Proceedings of the Third International Conference on Genetic Algorithms, pp. 104-9.
16. Goldberg, D. (1989), "Genetic algorithms in search", Optimization, and Machine Learning, Addison-Wesley, Reading, MA.
17. Goldberg, D. and Lingle, R. (1985), "Alleles, loci, and the traveling salesman problem", Proceedings of the 1st International Conference on Genetic Algorithms and their Applications, pp. 154-9.
18. Grefenstette, J.J. (1986), "Optimization of control parameters for genetic algorithms", IEEE Transactions on Systems, Man, and Cybernetics, SMC, Vol. 16 No. 1, pp. 122-8.
19. Herrera, F. and Lozano, M. (1996), "Adaptive genetic algorithms based on fuzzy techniques", Proceedings of IPMU'96, Granada, pp. 775-80.
20. Hinterding, R. (1997), "Self-adaptation using Multi- chromosomes", Proceedings of the 4th IEEE International Conference on Evolutionary Computation, IEEE Press, pp. 87-91.
21. Hinterding, R., Michalewicz, Z. and Eiben, A.E. (1997), "Adaptation in evolutionary computation: a survey", Proceedings of the IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence.
22. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press, Cambridge, MA.
23. Julstrom, B.A. (1995), "What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm", Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 81-7.
24. Levenick, J.R. (1995), "Metabits: generic endogenous crossover control", Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, pp. 88-95.
25. MacGiollaBhride, F., McGinnity, T.M. and McDaid, L.J. (2003), "Adaptive reasoning for genetic algorithms: a new strategy", paper presented at: 2nd IEEE Systems, Man & Cybernetics UK & RI Conference on Cybernetic Intelligence, Challenges and Advances, pp. 45-50.
26. Martin, O., Gras, R., Hernandez, D. and Appel, R.D. (2003), "Optimizing genetic algorithms using self-adaptation and explored space modelization", paper presented at The Fifth International Workshop on Frontiers in Evolutionary Algorithms, pp. 291-4.
27. Muhlenbein, H. (1992), "How genetic algorithms really work: I. Mutation and hillclimbing", Parallel Problem Solving from Nature 2, pp. 15-29.
28. Mühlenbein, J., Schomisch, U. and Born, J. (1991), "The parallel genetic algorithm as function optimizer", Parallel Computing, Vol. 17, pp. 619-32.
29. Rastrigin, L.A. (1974), Systems of Extremal Control, Nauka, Moscow.
30. Rechenberg, I. (1973), "Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution", Frommann-Holzboog Verlag, Stuttgart.
31. Rogers, A. and Prügel-Bennett, A. (2000), "The dynamics of a genetic algorithm on a model hard optimization problem", Complex Systems, Vol. 11 No. 6, pp. 437-64.
32. Schaffer, J.D. and Morishima, A. (1987), "An adaptive crossover distribution mechanism for genetic algorithms", Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 36-40.
33. Schaffer, J.D., Caruana, R., Eshelman, L.J. and Das, R. (1989), "A study of control parameters affecting the online performance of genetic algorithms for function optimization", Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 51-60.
34. Smith, J. and Fogarty, T.C. (1996), "Self adaptation of mutation rates in a steady state genetic algorithm", paper presented at International Conference on Evolutionary Computation, pp. 318-23.
35. Spears, W.M. (1995a), "Adapting crossover in evolutionary algorithms", Proceedings of the Fourth Annual Conference on Evolutionary Programming, MIT Press, Cambridge, MA, pp. 367-84.
36. Spears, W.M. (1995b), "Adapting crossover in a genetic algorithm", Proceedings of the Fourth Annual Conference on Evolutionary Programming, MIT Press, Cambridge, MA, pp. 367-84.
37. Spears, W.M. (1997), "Recombination parameters", The Handbook of Evolutionary Computation.
38. Spears, W.M. and Anand, V. (1991), "A study of crossover operators in genetic programming", Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems, pp. 409-18.
39. Srinivas, M. and Patnaik, L.M. (1994), "Adaptive probabilities of crossover and mutation in genetic algorithms", IEEE Transactions on Systems, Man and Cybernetics, Vol. 24 No. 4, pp. 656-67.
40. Stracuzzi and David, J. (1998), "Some methods for the parallelization of genetic algorithms", available at: www.cs.umass.edu/ ∼ stracudj/genetic/dga.html.
41. Syswerda, G. (1989), "Uniform crossover in genetic algorithms", Proceedings of the Third International Conference on Genetic Algorithms, Morgan-Kaufmann, San Mateo, CA, pp. 2-9.
42. Tuson, A. and Ross, P. (1996), "Cost based operator rate adaptation: an investigation", Parallel Problem Solving from Nature - PPSN IV, Springer, Berlin, pp. 461-9.
43. Tuson, A. and Ross, P. (1998), "Adapting operator settings in genetic algorithms", Evolutionary Computation, Vol. 6 No. 2, pp. 161-84.
44. Wolpert, D.H. and Macready, W.G. (1995), "No free lunch theorems for search", Tech. Rep, Santa Fe, NM.
45. Wolpert, D.H. and Macready, W.G. (1997), "No free lunch theorems for optimization", IEEE Transactions on Evolutionary Computation, Vol. 1 No. 1, pp. 67-82.
46. Wu, Q.H., Cao, Y.J. and Wen, J.Y. (1998), "Optimal reactive power dispatch using an adaptive genetic algorithm", International Journal of Electrical Power & Energy Systems, Vol. 20 No. 8, pp. 563-9.
47. Yang, S. (2002), "Adaptive non-uniform crossover based on statistics for genetic algorithms", Proceedings of the Genetic and Evolutionary Computation Conf., GECCO 2002, Morgan Kaufmann Publishers, San Francisco, CA, pp. 650-7.
48. Yao, X., Liu, Y. and Liu, G. (1999), "Evolutionary programming made faster", IEEE Transactions on Evolutionary Computation, pp. 82-102.
49. Zhou, X., Cheng, H. and Ju, P. (2002), "The third-order induction motor parameter estimation using an adaptive genetic algorithm", Proceedings of the 4th World Congress on Intelligent Control and Automation, Vol. 2, pp. 1480-4.