Getting the best of both worlds: Discrete and continuous genetic and evolutionary algorithms in concert

Information Sciences

Volumn 156, Issue 3-4, 2003, Pages 147-171

  Pelikan, Martin ^a   Goldberg, David E ^a   Tsutsui, Shigeyoshi ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Adaptive mutation; Bayesian optimization algorithm; Continuous optimization; Evolution strategies; Linkage learning

Indexed keywords

GENETIC ALGORITHMS; OPTIMIZATION; VECTORS;

ADAPTIVE MUTATION;

INFORMATION SCIENCE;

EID: 0141844529     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(03)00174-9     Document Type: Article

References (42)

1. Bäck T. Evolutionary algorithms in theory and practice. 1996;Oxford University Press, New York.
2. Beyer H.-G. Toward a theory of evolution strategies: On the benefit of sex - the. (μ/μ,λ) -theory Evolutionary Computation. 3(1):1995;81-111.
3. Beyer H.-G. Toward a theory of evolution strategies: Self-adaptation. Evolutionary Computation. 3(3):1996;311-347.
4. P.A. Bosman, D. Thierens, Continuous iterated density estimation evolutionary algorithms within the IDEA framework. Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), 2000, pp. 197-200.
5. E. Cantú-Paz, Supervised and unsupervised discretization methods for evolutionary algorithms. Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 2001, pp. 213-216.
6. R. Etxeberria, P. Larrañaga, Global optimization using Bayesian networks. Second Symposium on Artificial Intelligence (CIMAF-99), 1999, pp. 332-339.
7. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning. 1989;Addison-Wesley, Reading, MA.
8. Goldberg D.E. Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems. 5(2):1991;139-167.
9. D.E. Goldberg, K. Deb, H. Kargupta, G. Harik, Rapid, accurate optimization of difficult problems using fast messy genetic algorithms, in: S. Forrest (Ed.), Proceedings of the International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993, pp. 56-64.
10. D.E. Goldberg, K. Deb, B. Korb, Don't worry, be messy, in: Proceedings of the International Conference on Genetic Algorithms (ICGA-91), 1991, pp. 24-30.
11. N. Hansen, A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, in: Proceedings of the International Conference on Evolutionary Computation (ICEC-96), 1996, pp. 312-317.
12. N. Hansen, A. Ostermeier, A. Gawelczyk, On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation, in: Proceedings of the International Conference on Genetic Algorithms (ICGA-95), 1995, pp. 57-64.
13. G. Harik, Linkage learning via probabilistic modeling in the ECGA (IlliGAL Report No. 99010), University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1999.
14. Harik G.R., Goldberg D.E. Learning linkage. Foundations of Genetic Algorithms. 4:1996;247-262.
15. D. Heckerman, D. Geiger, D.M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data (Technical Report MSR-TR-94-09), Microsoft Research, Redmond, WA, 1994.
16. Holland J.H. Adaptation in Natural and Artificial Systems. 1975;University of Michigan Press, Ann Arbor, MI.
17. R.A. Howard, J.E. Matheson, Influence diagrams, in: R.A. Howard, J.E. Matheson (Eds.), Readings on the principles and applications of decision analysis, vol. II. Strategic Decisions Group, Menlo Park, CA, 1981, pp. 721-762.
18. H. Kargupta, SEARCH, polynomial complexity, and the fast messy genetic algorithm. Doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL, 1995.
19. H. Kargupta, The gene expression messy genetic algorithm, in: Proceedings of the International Conference on Evolutionary Computation (ICEC-96), 1996, pp. 814-819.
20. Larrañaga P., Lozano J.A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. 2002;Kluwer, Boston, MA.
21. Mühlenbein H., Paaß G. From recombination of genes to the estimation of distributions I. Binary parameters. Parallel Problem Solving from Nature. 1996;178-187.
22. Munetomo M., Goldberg D.E. Linkage identification by non-monotonicity detection for overlapping functions. Evolutionary Computation. 7(4):1999;377-398.
23. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. 1988;Morgan Kaufmann, San Mateo, CA.
24. M. Pelikan, Bayesian optimization algorithm: From single level to hierarchy, Doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL, 2002.
25. M. Pelikan, D.E. Goldberg, E. Cantú-Paz, Linkage problem, distribution estimation, and Bayesian networks (IlliGAL Report No. 98013), University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1998.
26. M. Pelikan, D.E. Goldberg, E. Cantú-Paz, BOA: The Bayesian optimization algorithm, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), I, pp. 525-532 (Also IlliGAL Report No. 99003, 1999).
27. M. Pelikan, D.E. Goldberg, E. Cantú-Paz, BOA: The Bayesian optimization algorithm, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), I, pp. 525-532 (Also IlliGAL Report No. 99003, 1999).
28. M. Pelikan, D.E. Goldberg, E. Cantú-Paz, Bayesian optimization algorithm, population sizing, and time to convergence, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 275-282 (Also IlliGAL Report No. 2000001, 2000).
29. M. Pelikan, D.E. Goldberg, E. Cantú-Paz, Bayesian optimization algorithm, population sizing, and time to convergence, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 275-282 (Also IlliGAL Report No. 2000001, 2000).
30. Pelikan M., Goldberg D.E., Lobo F. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications. 21(1):2002;5-20. Also IlliGAL Report No. 99018.
31. Pelikan M., Goldberg D.E., Lobo F. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications. 21(1):2002;5-20. Also IlliGAL Report No. 99018.
32. M. Pelikan, D.E. Goldberg, K. Sastry, Bayesian optimization algorithm, decision graphs, and Occam's razor (IlliGAL Report No. 2000020), University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2000.
33. Rechenberg I. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 1973;Frommann-Holzboog, Stuttgart.
34. Rechenberg I. Evolutionsstrategie '94. 1994;Frommann-Holzboog Verlag, Stuttgart.
35. F. Rothlauf, Towards a theory of representations for genetic and evolutionary algorithms: Development of basic concepts and their application to binary and tree representations, Doctoral dissertation, University of Bayreuth, Beyreuth, Germany, 2001.
36. K. Sastry, Efficient atomic cluster optimization using a hybrid extended compact genetic algorithm with seeded population (IlliGAL Report No. 2001018), University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2001.
37. J. Schwarz, J. Ocenasek, A problem-knowledge based evolutionary algorithm KBOA for hypergraph partitioning, in: Proceedings of the Fourth Joint Conference on Knowledge-Based Software Engineering, IO Press, Brno, Czech Republic, 2000, pp. 51-58.
38. H.-P. Schwefel, Adaptive Mechanismen in der biologischen Evolution und ihr Einfluss auf die Evolutionsgeschwindigkeit (Technical Report of the Working Group of Bionics and Evolution Techniques at the Institute for Measurement and Control Technology Re 215/3), Technical University of Berlin, Berlin, July 1974.
39. H.-P. Schwefel, Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie, in: Interdisciplinary Systems Research, vol. 26, Birkhäuser, Basle, Switzerland, 1977.
40. Schwefel H.-P. Numerical Optimization of Computer Models. 1981;John Wiley and Sons, New York.
41. H.-P. Schwefel, Contemporary evolution strategies, Advances in Artificial Life, Third International Conference on Artificial Life, 1995.
42. S. Tsutsui, M. Pelikan, D.E., Goldberg, Evolutionary algorithm using marginal histogram models in continuous domain (IlliGAL Report No. 2001019), University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2001