Phenotype Search Trajectory Networks for Linear Genetic Programming

TL;DR

This paper visualizes and analyzes the search trajectories of genetic programming in genotypic and phenotypic spaces, revealing how complexity and abundance influence evolutionary accessibility and progression.

Contribution

It introduces graph-based models for visualizing search trajectories and links phenotypic complexity and genotypic abundance to evolutionary dynamics.

Findings

More complex phenotypes are under-represented and harder to discover.

Less complex phenotypes are over-represented and serve as evolutionary stepping-stones.

Phenotypic complexity correlates with genotypic abundance and search difficulty.

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. 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.