Genetic Programming Convergence

author = "W. B. Langdon",

title = "Genetic Programming Convergence",

journal = "Genetic Programming and Evolvable Machines",

year = "2022",

volume = "23",

number = "1",

pages = "71--104",

month = mar,

keywords = "genetic algorithms, genetic programming, evolutionary computation, stochastic search, diversity, bottom up incremental evaluation, PIE, propagation, infection, and execution, SIMD parallel processing, AVX vector instructions",

ISSN = "1389-2576",

URL = "

http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_GPEM_gpconv.pdf",

URL = "

https://rdcu.be/cwoIQ",

DOI = "

doi:10.1007/s10710-021-09405-9",

video_url = "

https://youtu.be/irLoaq6MzbU",

size = "34 pages",

abstract = "We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems within existing memory use.",

notes = "Two page summary \cite{langdon:2022:GECCOhop_gpem}",