Phenotype Search Trajectory Networks for Linear Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Misc{hu:2022:pstnLGP,
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author = "Ting Hu and Gabriela Ochoa and Wolfgang Banzhaf",
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title = "Phenotype Search Trajectory Networks for Linear
Genetic Programming",
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howpublished = "ArXiv",
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year = "2022",
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month = "15 " # nov,
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keywords = "genetic algorithms, genetic programming, Neutral
networks, Genotype-to-phenotype mapping, Al-gorithm
modeling, Algorithm analysis, Search trajectories,
Complexnetworks, Visualisation, Kolmogorov complexity,
Populations and Evolution (q-bio.PE), Artificial
Intelligence (cs.AI), FOS: Biological sciences, FOS:
Biological sciences, FOS: Computer and information
sciences, FOS: Computer and information sciences",
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URL = "https://arxiv.org/abs/2211.08516",
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DOI = "doi:10.48550/ARXIV.2211.08516",
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size = "16 pages",
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abstract = "Genotype-to-phenotype mappings translate genotypic
variations such as mutations into phenotypic changes.
Neutrality is the observation that some mutations do
not lead to phenotypic changes. Studying the search
trajectories in genotypic and phenotypic spaces,
especially through neutral mutations, helps us to
better understand the progression of evolution and its
algorithmic behaviour. we visualise the search
trajectories of a genetic programming system as
graph-based models, where nodes are
genotypes/phenotypes and edges represent their
mutational transitions. We also quantitatively measure
the characteristics of phenotypes including their
genotypic abundance (the requirement for neutrality)
and Kolmogorov complexity. We connect these quantified
metrics with search trajectory visualisations, and find
that more complex phenotypes are under-represented by
fewer genotypes and are harder for evolution to
discover. Less complex phenotypes, on the other hand,
are over-represented by genotypes, are easier to find,
and frequently serve as stepping-stones for
evolution.",
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copyright = "Creative Commons Attribution 4.0 International",
- }
Genetic Programming entries for
Ting Hu
Gabriela Ochoa
Wolfgang Banzhaf
Citations