Genetic_Programming_Theory_and_Practice_XIII

(C. Jardin) #1

Evolving Simple Symbolic Regression Models 19


Luke S, Panait L (2002) Lexicographic Parsimony Pressure. In: Langdon WB, Cantú-Paz E,
Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull
L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) Proceedings of the genetic
and evolutionary computation conference (GECCO’2002). Morgan Kaufmann Publishers, San
Francisco, CA, pp 829–836
Poli R (2010) Covariant Tarpeian method for bloat control in genetic programming. Genet Program
Theory Pract VIII 8:71–90
Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via
http://lulu.comand freely available athttp://www.gp-field-guide.org.uk
Silva S, Costa E (2009) Dynamic limits for bloat control in genetic programming and a review of
past and current bloat theories. Genet Program Evolvable Mach 10(2):141–179
Smits GF, Kotanchek M (2005) Pareto-front exploitation in symbolic regression. In: Genetic
programming theory and practice II. Springer, Berlin, pp 283–299
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic
algorithms. Evol Comput 2(3):221–248
Vanneschi L, Castelli M, Silva S (2010) Measuring bloat, overfitting and functional complexity
in genetic programming. In: Proceedings of the 12th annual conference on genetic and
evolutionary computation. ACM, New York, pp 877–884
Vladislavleva EJ, Smits GF, Den Hertog D (2009) Order of nonlinearity as a complexity measure
for models generated by symbolic regression via Pareto genetic programming. IEEE Trans Evol
Comput 13(2):333–349
Wagner S (2009) Heuristic optimization software systems - modeling of heuristic optimization
algorithms in the heuristiclab software environment. Ph.D. thesis, Institute for Formal Models
and Verification, Johannes Kepler University, Linz
White DR, McDermott J, Castelli M, Manzoni L, Goldman BW, Kronberger G, Jaskowski W,
O’Reilly UM, Luke S (2013) Better GP benchmarks: community survey results and proposals.
Genet Program Evol Mach 14(1):3–29. doi:10.1007/s10710-012-9177-2

Free download pdf