Abstract
Genetic programming gradually assembles high-level structures from low-level entities or building blocks. This chapter describes methods for investigating emergent phenomena in genetic programming by looking at a population’s collective behavior. It details how these methods can be used to trace genotypic changes across lineages and genealogies. Part of the methodology, we present an algorithm for decomposing arbitrary subtrees from the population to their inherited parts, picking up the changes performed by either crossover or mutation across ancestries. This powerful tool creates new possibilities for future theoretical investigations on evolutionary algorithm behavior concerning building blocks and fitness landscape analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Altenberg, L.: Emergent phenomena in genetic programming. In: Sebald, A.V., Fogel, L. J. (eds.) Evolutionary Programming—Proceedings of the Third Annual Conference, pp. 233–241, pp. 24–26. World Scientific Publishing, San Diego (1994)
Altenberg, L.: The Schema Theorem and Price’s Theorem. Foundations of Genetic Algorithms, pp. 23–49. Morgan Kaufmann (1995)
Angeline, P.J.: Genetic programming and emergent intelligences. In: Kinnear Jr, K.E. (ed.) Advances in Genetic Programming, pp. 75–98. MIT Press, Cambridge (1994)
Banzhaf, W.: Genetic programming and emergence. Genet. Program. Evol. Mach.15(1), 63–73 (2014)
Burlacu, B., Affenzeller, M., Kommenda, M., Winkler, S., Kronberger, G.: Visualization of genetic lineages and inheritance information in genetic programming. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO’13 Companion, pp. 1351–1358, ACM (2013)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Michigan (1975)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
O’Reilly, U.-M., Oppacher F.: The troubling aspects of a building block hypothesis for genetic programming. In: Whitley, L.D., Vose, M.D. (eds.), Foundations of Genetic Algorithms 3, pp. 73–88, Estes Park, Colorado, 1994. Morgan Kaufmann. Published (1995)
Poli, R., Langdon, W.B., Dignum, S.: On the limiting distribution of program sizes in tree-based genetic programming. Technical Report CSM-464, Department of Computer Science, University of Essex (2006)
Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: Part I. Evol. Comput. 11(1), 53–66 (2003)
Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: Part II. Evol. Comput. 11(2), 169–206 (2003)
Spector, L., Langdon, W.B., O’Reilly, U.-M., Angeline, P.J. (eds.): Advances in Genetic Programming. MIT Press, Cambridge (1999)
Stadler, P.F.: Genotype-phenotype maps. BIOLOGICAL THEORY, 2, 2006
Wagner, G.P., Altenberg, L.: Complex adaptations and the evolution of evolvability. Evolution 50(3), 967–976 (1996)
Wagner, S., Affenzeller, M.: The heuristiclab optimization environment. Technical report, Johannes Kepler University Linz, Austria, 2004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Burlacu, B., Affenzeller, M., Winkler, S., Kommenda, M., Kronberger, G. (2015). Methods for Genealogy and Building Block Analysis in Genetic Programming. In: Borowik, G., Chaczko, Z., Jacak, W., Łuba, T. (eds) Computational Intelligence and Efficiency in Engineering Systems. Studies in Computational Intelligence, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-15720-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-15720-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15719-1
Online ISBN: 978-3-319-15720-7
eBook Packages: EngineeringEngineering (R0)