Phenotype Search Trajectory Networks for Linear Genetic Programming
This work addresses the challenge of analyzing evolutionary algorithms for researchers in genetic programming, but it is incremental as it builds on existing concepts of neutrality and search visualization.
The study tackled the problem of understanding evolutionary search in genetic programming by visualizing search trajectories as graphs and quantifying phenotype characteristics like genotypic abundance and Kolmogorov complexity. The result showed that more complex phenotypes are under-represented and harder to discover, while less complex ones are over-represented and act as stepping-stones.
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.