Redundant Representations in Evolutionary Computation

Evolutionary Computation

Volumn 11, Issue 4, 2003, Pages 381-415

  Rothlauf, Franz ^a   Goldberg, David E ^b  

^a UNIVERSITY OF MANNHEIM   (Germany)

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

Author keywords

Link and node biased encoding; Neutral networks; Neutral theory; Non synonymously redundant; Redundant representations; Synonymously redundant; Trivial voting mapping

Indexed keywords

TRIVIAL VOTING MAPPING;

ENCODING (SYMBOLS); GENETIC ALGORITHMS; MAPPING; MATHEMATICAL MODELS; PROBLEM SOLVING;

EVOLUTIONARY ALGORITHMS;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BIOLOGY; EVOLUTION; GENOTYPE; PHENOTYPE; POPULATION DENSITY; TIME;

ALGORITHMS; COMPUTATIONAL BIOLOGY; EVOLUTION; GENOTYPE; MODELS, GENETIC; PHENOTYPE; POPULATION DENSITY; TIME FACTORS;

EID: 0344946467     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/106365603322519288     Document Type: Article

References (74)

1. Abramowitz, M., & Stegun, I. A. (1972). Handbook of mathematical functions. New York: Dover Publications.
2. Abuali, F. N., Wainwright, R. L., & Schoenefeld, D. A. (1995). Determinant factorization: A new encoding scheme for spanning trees applied to the probabilistic minimum spanning tree problem. See Eschelman (1995), pp. 470-477.
3. Ackley, D. H. (1987). A connectionist machine for genetic hill climbing. Boston: Kluwer Academic.
4. Banzhaf, W. (1994). Genotype-phenotype-mapping and neutral variation - A case study in genetic programming. See Davidor, Schwefel, and Männer (1994), pp. 322-332.
5. Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., & Smith, R. E. (Eds.) (1999). Proceedings of the Genetic and Evolutionary Computation Conference: Volume 1. San Francisco, CA: Morgan Kaufmann Publishers.
6. Barnett, L. (1997). Tangled webs: Evolutionary dynamics on fitness landscapes with neutrality. Master's thesis, School of Cognitive Sciences, University of East Sussex, Brighton.
7. Barnett, L. (1998, June 27-29). Ruggedness and neutrality: The NKp family of fitness landscapes. In Adami, C., Belew, R. K., Kitano, H., & Taylor, C. (Eds.), Proceedings of the 6th International Conference on Artificial Life (ALIFE-98) (pp. 18-27). Cambridge, MA, USA: MIT Press.
8. Barnett, L. (2001). Netcrawling - optimal evolutionary search with neutral networks. In Proceedings of the 2001 Congress on Evolutionary Computation CEC01 (pp. 30-37). Piscataway, NJ: IEEE Press.
9. Belew, R. K., & Booker, L. B. (Eds.) (1991). Proceedings of the Fourth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann.
10. Cohoon, J. P., Hegde, S. U., Martin, W. N., & Richards, D. (1988). Floorplan design using distributed genetic algorithms. In IEEE International Conference on Computer Aided-Design (pp. 452-455). IEEE.
11. Darwin, C. (1859). On the origin of species. London: John Murray.
12. Dasgupta, D. (1995). Incorporating redundancy and gene activation mechanisms in genetic search for adapting to non-stationary environments. In Chambers, L. (Ed.), Practical Handbook of Genetic Algorithms (Chapter 13, pp. 303-316). CRC Press.
13. Davidor, Y., Schwefel, H.-P, & Männer, R. (Eds.) (1994). Parallel Problem Solving from Nature- PPSN III. Berlin: Springer-Verlag.
14. Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Schaffer, J. D. (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp. 61-69). San Mateo, CA: Morgan Kaufmann.
15. Deb, K., Altenberg, L., Manderick, B., Bäck, T., Michalewicz, Z., Mitchell, M., & Forrest, S. (1997). Theoretical foundations and properties of evolutionary computations: fitness landscapes. In B̈ack, T., Fogel, D. B., & Michalewicz, Z. (Eds.), Handbook of Evolutionary Computation (pp. B2.7:1-B2.7:25). Bristol and New York: Institute of Physics Publishing and Oxford University Press.
16. Deb, K., & Goldberg, D. E. (1993). Analyzing deception in trap functions. In Whitley, L. D. (Ed.), Foundations of Genetic Algorithms 2 (pp. 93-108). San Mateo, CA: Morgan Kaufmann.
17. Ebner, M., Langguth, P., Albert, J., Shackleton, M., & Shipman, R. (2001, 27-30 May). On neutral networks and evolvability. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001 (pp. 1-8). COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea: IEEE Press.
18. Eschelman, L. (Ed.) (1995). Proceedings of the Sixth International Conference on Genetic Algorithms. San Francisco, CA: Morgan Kaufmann.
19. Eshelman, L. J., & Schaffer, J. D. (1991). Preventing premature convergence in genetic algorithms by preventing incest. See Belew and Booker (1991), pp. 115-122.
20. Gaube, T., & Rothlauf, F. (2001). The link and node biased encoding revisited: Bias and adjustment of parameters. In Boers, E. J. W., Cagnoni, S., Gottlieb, J., Hart, E., Lanzi, P. L., Raidl, G. R., Smith, R. E., & Tijink, H. (Eds.), Applications of evolutionary Computing; Proc. EvoWorkshops 2001 (pp. 1-10). Berlin: Springer.
21. Gerrits, M., & Hogeweg, P. (1991). Redundant coding of an NP-complete problem allows effective Genetic Algorithm search. In Schwefel, H.-P, & Männer, R. (Eds.), Parallel Problem Solving from Nature (pp. 70-74). Berlin: Springer-Verlag.
22. Goldberg, D. E., Deb, K., & Clark, J. H. (1992). Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6, 333-362.
23. Gottlieb, J., Julstrom, B. A., Raidl, G. R., & Rothlauf, F. (2001). Prüfer numbers: A poor representation of spanning trees for evolutionary search. In Spector, L., E., G., Wu, A., B., L. W., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M., & Burke, E. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference 2001 (pp. 343-350). San Francisco, CA: Morgan Kaufmann Publishers.
24. Hamming, R. (1980). Coding and information theory. Prentice-Hall.
25. Harik, G. R., Cantú-Paz, E., Goldberg, D. E., & Miller, B. L. (1997). The gambler's ruin problem, genetic algorithms, and the sizing of populations. In Bäck, T. (Ed.), Proceedings of the Forth International Conference on Evolutionary Computation (pp. 7-12). New York: IEEE Press.
26. Harvey, L., & Thompson, A. (1997). Through the labyrinth evolution finds a way: A silicon ridge. Lecture Notes in Computer Science, 1259, 406-422.
27. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
28. Horn, J. (1995). Genetic algorithms, problem difficulty, and the modality of fitness land-scapes (IlliGAL Report No. 95004). Urbana, IL: University of Illinois at Urbana-Champaign.
29. Huynen, M. (1996). Exploring phenotype space through neutral evolution. J. Mol. Evol., 43, 165-169.
30. Huynen, M., Stadler, P., & Fontana, W. (1996). Smoothness within ruggedness: The role of neutrality in adaptation. In Proceedings of the National Academy of Sciences of the USA, 1993 (pp. 397-401). Washington, D.C.: National Academy of Sciences.
31. Jones, T., & Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. See Eschelman (1995), pp. 184-192.
32. Julstrom, B. A. (1999). Redundant genetic encodings may not be harmful. See Banzhaf, Daida, Eiben, Garzon, Honavar, Jakiela, and Smith (1999), pp. 791.
33. Kargupta, H. (2000). The genetic code-like transformations and their effect on learning functions. See Schoenauer, Deb, Rudolph, Yao, Lutton, Merelo, and Schwefel (2000), pp. 99-108.
34. Kargupta, H. (2001). A striking property of genetic code-like transformations. Complex Systems, 13(1), 1-32.
35. Kimura, M. (1983). The neutral theory of molecular evolution. Cambridge University Press.
36. Knowles, J. D., & Watson, R. A. (2002). On the utility of redundant encodings in mutation-based evolutionary search. In Merelo, J. J., Adamidis, P., Beyer, H.-G., Fernandez-Villacanas, J.-L., & Schwefel, H.-P. (Eds.), Parallel Problem Solving from Nature, PPSN VII (pp. 88-98). Berlin: Springer-Verlag.
37. Krishnamoorthy, M., & Ernst, A. T. (2001). Comparison of algorithms for the degree constrained minimum spanning tree. Journal of Heuristics, 7, 587-611.
38. Manderick, B., de Weger, M., & Spiessens, P. (1991). The genetic algorithm and the structure of the fitness landscape. See Belew and Booker (1991), pp. 143-150.
39. Mayr, E. (1991). One long argument: Charles Darwin and the genesis of modern evolutionary thought. Cambridge, Massachusetts: Harvard University Press.
40. Miller, B. L., & Goldberg, D. E. (1996a). Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 4(2), 113-131.
41. Miller, B. L., & Goldberg, D. E. (1996b). Optimal sampling for genetic algorithms. In Dagli, C. H., Akay, M., Chen, C. L. P., Fernández, B. R., & Ghosh, J. (Eds.), Proceedings of the Artificial Neural Networks in Engineering (ANNIE '96) conference, Volume 6 (pp. 291-297). New York: ASME Press.
42. Mühlenbein, H., & Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm: I. Continuous parameter optimization. Evolutionary Computation, 1(1), 25-49.
43. O'Neill, M., & Ryan, C. (2001). Grammatical evolution. IEEE Transactions on Evolutionary Computation, 5(4), 349-358.
44. Palmer, C. C. (1994). An approach to a problem in network design using genetic algorithms. unpublished PhD thesis, Polytechnic University, Troy, NY.
45. Palmer, C. C., & Kershenbaum, A. (1994a). Representing trees in genetic algorithms. In Proceedings of the First IEEE Conference on Evolutionary Computation, Volume 1 (pp. 379-384). Piscataway, NJ: IEEE Service Center.
46. Palmer, C. C., & Kershenbaum, A. (1994b). Two algorithms for finding optimal communication spanning trees. IBM research report RC-19394.
47. Prim, R. (1957). Shortest connection networks and some generalizations. Bell System Technical Journal, 36, 1389-1401.
48. Prüfer, H. (1918). Neuer Beweis eines Satzes über Permutationen. Archiv für Mathematik und Physik, 27, 742-744.
49. Raidl, G. R., & Julstrom, B. A. (2000). A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem. In Carroll, J., Damiani, E., Haddad, H., & Oppenheim, D. (Eds.), Proceedings of the 2000 ACM Symposium on Applied Computing (pp. 440-445). ACM Press.
50. Rana, S. B., & Whitley, L. D. (1997). Bit representations with a twist. In Bäck, T. (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (pp. 188-195). San Francisco: Morgan Kaufmann.
51. Reidys, C. M., & Stadler, P. F. (1998). Neutrality in fitness landscapes. Applied Mathematics and Computation, 117(2-3), 321-350.
52. Ronald, S., Asenstorfer, J., & Vincent, M. (1995). Representational redundancy in evolutionary algorithms. In 1995 IEEE International Conference on Evolutionary Computation, Volume 2 (pp. 631-636). Piscataway, NJ: IEEE Service Center.
53. Rothlauf, F. (2002). Representations for genetic and evolutionary algorithms. Number 104 in Studies on Fuzziness and Soft Computing. Berlin: Springer.
54. Rothlauf, F., & Goldberg, D. E. (2000). Prüfernumbers and genetic algorithms: A lesson on how the low locality of an encoding can harm the performance of GAs. See Schoenauer, Deb, Rudolph, Yao, Lutton, Merelo, and Schwefel (2000), pp. 395-404.
55. Rothlauf, F., Goldberg, D. E., & Heinzl, A. (2002). Network random keys - A tree network representation scheme for genetic and evolutionary algorithms. Evolutionary Computation, 10(1), 75-97.
56. Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J. J., & Schwefel, H.-P. (Eds.) (2000). Parallel Problem Solving from Nature, PPSN VI. Berlin: Springer-Verlag.
57. Schuster, P. (1997). Genotypes with phenotypes: Adventures in an RNA toy world. Biophys. Chem., 66, 75-110.
58. Shackleton, M., Shipman, R., & Ebner, M. (2000, 6-9 July). An investigation of redundant genotype-phenotype mappings and their role in evolutionary search. In Proceedings of the 2000 Congress on Evolutionary Computation CEC00 (pp. 493-500). La Jolla Marriott Hotel La Jolla, California, USA: IEEE Press.
59. Shipman, R. (1999). Genetic redundancy: Desirable or problematic for evolutionary adaptation? In Proceedings of the 4th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA) (pp. 1-11). Springer Verlag.
60. Shipman, R., Shackleton, M., Ebner, M., & Watson, R. (2000). Neutral search spaces for artificial evolution: A lesson from life. In Bedau, M., McCaskill, J., Packard, N., & Rasmussen, S. (Eds.), Proceedings of Artificial Life VII (pp. section III (Evolutionary and Adaptive Dynamic)). MIT Press.
61. Shipman, R., Shackleton, M., & Harvey, L. (2000). The use of neutral genotype-phenotype mappings for improved evoutionary search. British Telecom Technology Journal, 18(4), 103-111.
62. Smith, T., Husbands, P., & O'Shea, M. (2001a). Evolvability, neutrality and search space (Technical Report 535). School of Cognitive and Computing Sciences, University of Sussex.
63. Smith, T., Husbands, P., & O'Shea, M. (2001b). Neutral networks and evolvability with complex genotype-phenotype mapping. In Proceedings of the European Converence on Artificial Life: ECAL2001, Volume LNAI 2159 (pp. 272-281). Berlin: Springer.
64. Smith, T., Husbands, P., & O'Shea, M. (2001). Neutral networks in an evolutionary robotics search space. In of Electrical, I., & Engineers, E. (Eds.), Proceedings of 2001 IEEE International Conference on Evolutionary Computation (pp. 136-145). Piscataway, NJ: IEEE Service Center.
65. Thierens, D. (1995). Analysis and design of genetic algorithms. Leuven, Belgium: Katholieke Universiteit Leuven.
66. Thierens, D., & Goldberg, D. E. (1994). Convergence models of genetic algorithm selection schemes. See Davidor, Schwefel, and Männer (1994), pp. 119-129.
67. Toussaint, M., & Igel, C. (2002). Neutrality: A necessity for self-adaptation. In Fogel, D. B., El-Sharkawi, M. A., Yao, X., Greenwood, G., Iba, H., Marrow, P., & Shackleton, M. (Eds.), Proceedings of the 2002 Congress on Evolutionary Computation CEC2002 (pp. 1354-1359). IEEE Press.
68. Weinberger, E. (1990). Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics, 63, 325-336.
69. Whitley, D. (1999). A free lunch proof for gray versus binary encodings. See Banzhaf, Daida, Eiben, Garzon, Honavar, Jakiela, and Smith (1999), pp. 726-733.
70. Whitley, D. (2000). Functions as permutations: Implications for no free lunch, walsh analysis and statistics. See Schoenauer, Deb, Rudolph, Yao, Lutton, Merelo, and Schwefel (2000), pp. 169-178.
71. Whitley, D. (2002, September). Evaluating evolutionary algorithms. Tutorial Program at Parallel Problem Solving from Nature (PPSN 2002.
72. Whitley, D., Rana, S., & Heckendorn, R. (1997). Representation issues in neighborhood search and evolutionary algorithms. In Genetic Algorithms and Evolution Strategy in Engineering and Computer Science (Chapter 3, pp. 39-58). West Sussex, England: John Wiley & Sons Ltd.
73. Yu, T., & Miller, J. (2001). Neutrality and evolvability of Boolean function landscapes. In Proceedings of the 4th European Conference on Genetic Programming (EuroGP), Volume LNCS 2038 (pp. 204-217). Springer.
74. Yu, T., & Miller, J. (2002). Finding needles in haystacks is not hard with neutrality. In Proceedings of the 5th European Conference on Genetic Programming (EuroGP), Volume LNCS (pp. 13-25) Spnnger.