Jason M. Daida
ABSTRACT
This paper describes a tunably-difficult problem for genetic programming (GP) that probes for limits to building block mixing and assembly. The existence of such a problem can be used to garner insight into the dynamics of what happens during the course of a GP run. The results indicate that the amount of mixing is fairly low in comparison to the amount of content that could be present in an initial population.
- Banzhaf, W., et al. GP: An Introduction. Morgan Kaufmann, San Francisco, 1998.Google Scholar
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- Daida, J.M. Limits to Expression in Genetic Programming: Lattice-Aggregate Modeling. in CEC 2002, IEEE, Piscataway, 2002, 273--278.Google ScholarCross Ref
- Daida, J.M. Towards Identifying Populations that Increase the Likelihood of Success in Genetic Programming. in GECCO 2005, 2005. Google ScholarDigital Library
- Daida, J.M. What Makes a Problem GP-Hard? A Look at How Structure Affects Content. in Riolo, R.L. and Worzel, W. eds. GP Theory and Practice, Kluwer Academic Publishers, Dordrecht, 2003, 99--118.Google Scholar
- Daida, J.M., Bertram, R.B., Polito 2, J.A. and Stanhope, S.A. Analysis of Single-Node (Building) Blocks in GP. in Spector, L., et al. eds. Advances in GP 3, MIT Press, Cambridge, 1999, 217--241. Google ScholarDigital Library
- Daida, J.M. and Hilss, A.M. Identifying Structural Mechanisms in Standard GP. in Cantú-Paz, et al. eds. GECCO 2003, Springer-Verlag, Berlin, 2003, 1639--1651. Google ScholarDigital Library
- Daida, J.M., et al. What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes. in Cantú-Paz, et al. eds. GECCO 2003, Springer-Verlag, Berlin, 2003, 1665--1677. Google ScholarDigital Library
- Daida, J.M., et al. What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in GP. in Banzhaf, W., et al. eds. GECCO '99, Morgan Kaufmann, San Francisco, 1999, 982 -- 989.Google Scholar
- Daida, J.M., et al. What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in GP. GPEM, 2 (2). 165--191. Google ScholarDigital Library
- Gathercole, C. and Ross, P. An Adverse Interaction Between Crossover and Restricted Tree Depth in GP. in Koza, J.R., et al. eds. GP 1996, MIT Press, Cambridge, 1996, 291--296. Google ScholarDigital Library
- Goldberg, D.E. and O'Reilly, U.-M. Where Does the Good Stuff Go, and Why? in Banzhaf, W., et al. eds. EuroGP, Springer-Verlag, Berlin, 1998, 16--36. Google ScholarDigital Library
- Hall, J.M. and Soule, T. Does GP Inherently Adopt Structured Design Techniques? in O'Reilly, U.-M., et al. eds. GP Theory and Practice II, Kluwer Academic Publishers, Boston, 2004.Google Scholar
- Langdon, W.B. and Poli, R. An Analysis of the MAX Problem in Genetic Programming. in Koza, J.R., et al. eds. GP 1997, Morgan Kaufmann, San Francisco, 1997, 222--230.Google Scholar
- Langdon, W.B. and Poli, R. Foundations of GP. Springer-Verlag, Berlin, 2002.Google Scholar
- Matsumoto, M. and Nishimura, T. Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudorandom Number Generator. ACM Trans Mod and Comp Sim, 8 (1). 3--30. Google ScholarDigital Library
- McPhee, N.F. and Hopper, N.J. Analysis of Genetic Diversity through Population History. in Banzhaf, W., et al. eds. GECCO '99, Morgan Kaufmann, San Francisco, 1999, 1112 -- 1120.Google Scholar
- Motoki, T. Calculating the Expected Loss of Diversity of Selection Schemes. EC, 10 (4). 397--422. Google ScholarDigital Library
- O'Reilly, U.-M. The Impact of External Dependency in GP Primitives. in CEC 1999, IEEE Press, Piscataway, 1998, 306--311.Google Scholar
- O'Reilly, U.-M. and Goldberg, D.E. How Fitness Structure Affects Subsolution Acquisition in GP. in Koza, J.R., et al. eds. GP 1998, Morgan Kaufmann, San Francisco, 1998, 269--277.Google Scholar
- Poli, R. General Schema Theory for GP with Subtree-Swapping Crossover. in Miller, J.F., et al. eds. EuroGP 2001, Springer-Verlag, Berlin, 2001, 143--159. Google ScholarDigital Library
- Punch, W., et al. The Royal Tree Problem, A Benchmark for Single and Multiple Population GP. in Angeline, P.J. and K.E. Kinnear, J. eds. Advances in GP, MIT Press, Cambridge, 1996, 299--316. Google ScholarDigital Library
- Rosca, J.P. Analysis of Complexity Drift in GP. in Koza, J.R., et al. eds. GP 1997, Morgan Kaufmann, San Francisco, 1997, 286--294.Google Scholar
- Sastry, K., et al. Population Sizing for GP Based on Decision Making. in O'Reilly, U.-M., et al. eds. GP Theory and Practice II, Kluwer Academic, Boston, 2004, 49--65.Google Scholar
- Soule, T., et al. Code Growth in Genetic Programming. in Koza, J.R., et al. eds. GP 1996, MIT Press, Cambridge, 1996, 215 -- 223. Google ScholarDigital Library
- Zongker, D. and Punch, W. lilgp, Michigan State University Genetic Algorithms Research and Applications Group, Lansing, 1995.Google Scholar
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