TY - JOUR
T1 - Methods for genealogy and building block analysis in genetic programming
AU - Burlacu, Bogdan
AU - Affenzeller, Michael
AU - Winkler, Stephan
AU - Kommenda, Michael
AU - Kronberger, Gabriel
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84925003279&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15720-7_5
DO - 10.1007/978-3-319-15720-7_5
M3 - Article
SN - 1860-949X
VL - 595
SP - 61
EP - 74
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -