In this study, 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.
CITATION STYLE
Hu, T., Ochoa, G., & Banzhaf, W. (2023). Phenotype Search Trajectory Networks for Linear Genetic Programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13986 LNCS, pp. 52–67). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-29573-7_4
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